Session Themes and Outcomes

In this session we’re going to learn a few things about performing a differential expression analysis on gene expression data in R.

At the end of this session, you should be able to do the following tasks:

  1. Read a counts table into R including:
  1. Explore basic data paramaters such as library size
  2. Create a DGEList object
  3. Assess sample similarity and identify outliers using Unsupervised Clustering
  4. Perform a simple differential expression analysis

Data setup

Data QC

Estimation of library sizes

PCA/MDS assessment of outliers and batch effects

Differential expression

Now that we have our data in a nicely formatted file, we can move to R and follow a fairly standard workflow. Along the way, we’ll come across a few useful packages, data structures and coding tricks which will be applicable in many other contexts.

The packages we’ll use for this include not only the tidyverse, but a few other packages which are hosted on Bioconductor, such as limma, edgeR and AnnotationHub. We’ll also add the magrittr,pander and scales packages as they contain some useful additional utilities.

library(limma)
library(Glimma)
library(edgeR)
library(AnnotationHub)
library(tidyverse)
library(magrittr)
library(scales)
library(pander)
library(ggrepel)
library(RColorBrewer)

Data Setup

Import

First we should import the file we created using featureCounts. Please take a moment to check the location of the counts.out file you created in the last session to ensure the assignment of the location to the file object is correct.

file <- "../data/2_alignedData/featureCounts/counts.out"
rawCounts <- read_delim(file, comment = "#", delim = "\t")

This file has the gene identifiers in the first column, followed by the chromosome number, start, end, strand and length information (recall that we have subset our data to only include mouse chromosome 1), with the remaining columns containing the name of the bam file and the number of reads aligning to each gene. The first thing we might like to do is to remove the columns which don’t contain the count data we require today, and tidy up those column names.

countData <- dplyr::select(rawCounts, -c(Chr, Start, End, Strand, Length)) %>%
  set_colnames(basename(colnames(.))) %>%
  set_colnames(str_remove(colnames(.), ".trimmedAligned.sortedByCoord.out.bam"))

That looks much cleaner and we haven’t lost any important information but we’re not quite done. The group information we need for the differential expression is locked in the samplename. Next we can split the sample ID from the group information and save that information in a dataframe for later use.

sampleGroups <- as.data.frame(names(countData[-1]), stringsAsFactors = FALSE) %>%
  set_colnames(., "samplename") %>%
  tidyr::separate(., col = samplename, into = c("samplename", "group"), remove = TRUE)

Now we have a small table of metadata regarding our samples. We can also tidy up our countData table column names a little more by moving the gene IDs to row names and removing the group information after the sample name.

countData <- as.data.frame(countData) %>%
  column_to_rownames("Geneid")
colnames(countData) <- sampleGroups$samplename

Create a DGE List

The main object type we like to use for differential gene expression is a DGEList, which stands for Digital Gene Expression List, and is an object used by edgeR to store count data. This object is more complex than others we have looked at in R so far because it contains a number of slots for storing not only our data, but also different paramaters about the data. The DGEList objects have two mandatory elements, with the first being our counts data, and the second being our sample information. Recall that above we have made the gene IDs the row names and the sample IDs the column names.

Here’s one way to create a DGEList.

dgeList <- countData %>%
    DGEList() %>%
    calcNormFactors()

Here we have taken our counts table with gene IDs as row names and sample IDs as column names and put it in to a DGEList object, called dgeList, and called the calcNormFactors() function to calculated the normalisation factors, which can be seen in dgeList$samples.

If we decided to fit our counts using a negative binomial model (for discrete data), these norm.factors would be included in the model to adjust for variations in the library size and count distributions (for an interesting post on what “fitting” a model is see [fit] (https://diamondage.com/2017/06/03/what-does-it-mean-to-fit-a-model-anyway/)).

We have chosen to use a different approach for our analysis today, but taking the time to include the calculation of normalisation factors step as we form the DGEList object is still good practice as it allows us to change methodology part way through our planned analysis if we determine the negative binomial model is better suited to our data.

Take a moment now to run this next code chunk and inspect the samples data frame sitting inside the DGEList object we have just created.

dgeList$samples

R understood that the column names from our counts table contained the sample IDs for our project and created the samples data frame inside the DGEList object. lib.size and norm.factors have also been calculated from the counts table and incorporated into the element. We haven’t yet included any information describing our samples. In the samples element, we can set the group variable we previously took from the sample IDs by running:

dgeList$samples$group <- sampleGroups$group %>%
    factor(levels = c("cbc", "skm"))

Take another moment now to inspect the samples data frame again and you will now see what was previously a row of 1s is now populated with the appropriate tissue type.

dgeList$samples

As a small note of warning, keep in mind that we did not at any time alter the order in which our samples were being reported. This allowed us to simply cut the information from one table and paste it into another. There are ways of using mutating joins to ensure order is never corrupted including left_join, right_join, inner_join and others.

It is always worth taking the time to perform a “sanity check” to ensure the sample names and groups are correctly aligned before you continue.

Add Gene Information

A common, but optional element that we can include in a DGEList, is one called genes. This is where we can place information such as the genomic location, the gene symbol and anything else we think is going to be relevant. First, we need to find this information though, and we’ll use the package AnnotationHub which contains the metadata for all of the annotation packages included in Bioconductor.

ah <- AnnotationHub()

The structure of this new object ah is quite advanced. Take a moment to type in the object name and run the line, you’ll get an informative summary of all available annotation packages.

ah

Note that we have numerous data providers, species and data classes. The column on the left (AH####) is a shorthand ID we can use to retrieve any of these annotation objects.

We can also subset the Annotation Hub object to help us find what we’re looking for. In the following code, we’re restricting our search to mouse, and specifying Ensembl as our data source.

The final line looks for an object of class EnsDb which is a database object containing a large amount of information, but in a relatively user-friendly way.

ah <- ah %>%
  subset(species == "Mus musculus") %>%
  subset(dataprovider == "Ensembl") %>%
  subset(rdataclass == "EnsDb")

We have now subset ah and removed most of the information we don’t need (ie infromation related to other species and other data providors). The step is to ensure we are only using data from an appropriate release. ah currently contains data from release 87-92, which will be close enough to match the data we have generated so far (which was Ensembl 93). Ideally, these should be the same, but there is no EnsDb object for Ensembl 93. I’m sure one will be made available in the next release of Bioconductor. When we inspected ah above we saw that the latest release was AH60992 | Ensembl 92 EnsDb for Mus Musculus. Today we will use this annotation record and define it as the object ensDb

ensDb <- ah[["AH60992"]]
ensDb

There are many helper functions for extracting data from this package, such as transcripts(), promoters() and genes(). We want gene information today, so let’s just use that function. Note that after we obtain the data, we’re using subset() to ensure we only keep the data from chromosome 1.

genesGR <- genes(ensDb) %>%
    subset(seqnames == 1)

This object is a GenomicRanges (or GRanges) object and these are the bread & butter of genomic analysis in R. We could spend hours just looking at these, but the main point is that on the right of the gene IDs we have the chromosome (seqnames) followed by the range of bases the genes is contained within and the strand the gene is located on (note that start and end positions are both 1-based relative to the 5’ end of the plus strand of the choromosome, even when the range is on the minus strand). This is the core GRanges element. Run the following code chunk to return this information using the function granges()

granges(genesGR)

Underlying each GRanges object is a seqinfo object and these contain all of the genomic information about chromosome names and lengths. If comparing two GRanges objects, they must have identical seqinfo objects otherwise the comparison will return an error. This actually makes perfect sense, but can create issues when comparing data from UCSC, NCBI and Ensembl as they all use different formats for their chromosome names, even though they’re based on the same assemblies.

seqinfo(genesGR)

To the right of the pipes, we have the metadata columns, accessed using the function mcols(). Notice this returns a DataFrame which is a slightly more controlled version of a data.frame. The differences are beyond the scope of this course, but they can easily be coerced to a data.frame using as.data.frame. If you want to use the dplyr functions on them, you will need to go through this step.

mcols(genesGR)

Before we place this information into the genes element of our DGEList object we will remove columns that are redundant for our purposes today, and keep only the four most useful ones. We can achieve this by calling the mcols() function on our GRanges object. Firstly we use the square brackets to subset the genesGR object we created earlier, and then by using the assignment operator (<-) to overwrite this subsetted element back into the object.

mcols(genesGR) <- mcols(genesGR)[c("gene_id", "gene_name", "gene_biotype", "entrezid")]

If we wish to add this to our DGEList, note that the order of genes will be completely different. To fix this, we will use the rownames of our DGEList object to reorder the genes object. Taking the time to ensure that the two gene lists are in the same order is important because R will give no warning or error to inform us if the two gene lists are in different orders. R will not check that these two objects are compatible.

dgeList$genes <- genesGR[genesGR$gene_id %in% rownames(dgeList),]

Here we have used square brackets [] to subset and reorder the genesGR object such that it now only contains the genes in our dgeList object, and in the same order.

Now when we subset our DGEList by gene, the genes element will also be subset accordingly and the initial relationships will be maintained. Take a moment to inspect the first four elements of our dgeList object by running the code chunk below.

dgeList[1:4,]

You will notice that the degList$genes element is now populated with the information from annotationHub we downloaded earlier, and that dgeList$counts and degList$genes are in the same order.

Data QC

Undetectable genes

You may have noticed that no reads aligned to a number of the genes contained in this dataset. As common practice we remove genes with zero counts across all samples before we continue with our initial data exploration. Rather than inspect our data one line at a time looking for zeros we can use R to perform a logical test to see how many genes were not detected in our dataset. First we’ll add up the total counts for every gene and see how many received at least one count. In the following code chunk we will take the counts element from our DGEList object and pipe it into the function rowSums() which will output a named number vector with a single value (the sum of each gene) for each gene. This vector is in turn piped into another function is_greater_than() which will ask the logical question “is the value greater than the given value”, with the given value being 0. We then pipe the TRUE/FALSE answers to this logical question into table which will provide us with a simple two number summary.

dgeList$counts %>% 
    rowSums() %>%
    is_greater_than(0) %>%
    table

Any genes that fail our test (ie the sum of which are not greater than zero) will be counted in the FALSE column, and any genes that pass our test (ie the sum of which is greater than zero) will be counted in the TRUE column. We can see that a proportion of genes were not expressed or were undetectable in our original samples. A common approach would be to remove undetectable genes using some metric, such as Counts per Million reads, also known as CPM. We could consider a gene detectable if returning more than 1CPM, in every sample, from one (usually the smaller), of the treatment groups.

Although our dataset is small (6/8 libraries are < 1e6 reads), we usually deal with libraries between 20-30million reads. In the context of the more usual library size a CPM of 1 equates to 20-30 reads aligning to a gene, which we test in every sample from a treatment group. Here our smallest group is 4 so let’s see what would happen if we applied the filter of > 1 CPM in 4 samples.

First we’ll calculate the CPM for each gene in each sample, and then we’ll apply a logical test to each value checking whether it’s greater than 1. Next, we’ll add these up for each gene (i.e. row) and this will give us the total number of samples for each gene, that passed our criteria of CPM > 1. Finally, we’ll check for genes which passed our criteria in more than 4 samples, as our smallest group is 4. We could also have used the function is_weakly_greater_than() which would test for equality (\(\geq\)) instead of strictly greater than (\(>\)).

dgeList %>%
    cpm() %>%
    is_greater_than(1) %>%
    rowSums() %>%
    is_greater_than(4) %>%
    table()

Losing about 1/3 of the genes is pretty common, so let’s now apply that to our dataset. The object genes2keep below will be a logical vector deciding on whether we keep the gene for downstream analysis, based purely on whether we consider the gene to be detectable. We’ll create a new DGEList object by subsetting our primary one using the [] trick we learned earlier. We are creating a new object here so that if we change our mind about our filtering strategy, we don’t have to rerun all the code above.

genes2keep <- dgeList %>%
    cpm() %>%
    is_greater_than(1) %>%
    rowSums() %>%
    is_greater_than(4)

dgeList_Filtered <- dgeList[genes2keep,] %>%
  calcNormFactors()

Let’s compare the distributions of the two datasets (cbc and skm), using CPM on the log2 scale. In the following, the command par(mfrow = c(1,2)) is a base graphics approach and sets the plotting parameters to be a multi=feature layout, with 1 row and 2 columns. We set these paramaters because the convenient function plotDensities() we are calling below uses base graphics, not ggplot2.

col <- brewer.pal(nrow(dgeList$samples), "Paired")
par(mfrow = c(1,2))
dgeList %>%
    cpm(log = TRUE) %>%
    plotDensities(legend = FALSE,
                  main = "A. Before Filtering",
                  col=col)
dgeList_Filtered %>%
    cpm(log = TRUE) %>%
    plotDensities(legend = FALSE,
                  main = "B. After Filtering",
                  col=col)
Comparison of logCPM distributions before (A) and after (B) filtering for undetectable genes. Values on the x-axis represent logCPM

Comparison of logCPM distributions before (A) and after (B) filtering for undetectable genes. Values on the x-axis represent logCPM

par(mfrow = c(1,1))

Note the peak at the left in the first plot around zero. This is all of the genes with near-zero counts. Then note that this peak is missing the second plot, confirming that we have removed most of the undetectable genes. Ideally we would like to see that the peak around zero has been removed after filtering, and that the samples all have approximately the same distribution of counts. Keeping in mind that we have significantly subset our original fastq data during alignment earlier such that we only have genes from mouse chromosome 1.

Library Sizes

Next we should check our library sizes. It does appear that the two tissue types have a different profile of reads. There could be a number of reasons for seeing differences between groups including sequencing batch effects, differences in the RNA quality from different tissues or extractions, or lab operations being performed by different staff. This is not ideal but most modelling approaches will be able to handle this.

dgeList_Filtered$samples %>%
    ggplot(aes(group, lib.size, fill = group)) +
    geom_boxplot() +
    scale_y_continuous(labels = comma) +
    labs(x = "Tissue Type", y = "Library Size") +
    theme_bw() 
Library Sizes after filtering for undetectable genes.

Library Sizes after filtering for undetectable genes.

We’ve used he function comma from the scales package here to help us interpret the y-axis. For most vertebrate datasets we expect libraries of >20million reads, but as we’ve given you a significantly reduced dataset, these numbers are pretty acceptable.

Unsupervised clustering of samples

One of the first steps in exploring our gene expression data is that of unsupervised clustering in the form of a multi-dimensional scaling plot (MDS), Principal Component Analysis (PCA) or similar. By unsupervised here we mean to say that we haven’t told R how many groups we are expecting to see, or given any information about which samples we expect to see clustering closely together. Visually inspecting the data in this way allows us to start to understand the extent to which any differential expression can be detected before we have performed any statistical tests. It may also help us to identify any outliers in our samples, or perhaps a factor we haven’t yet considered that should be included when fitting our model later.

Multi-dimensional Scaling Plot (MDS)

An important first exploration of our sequencing data is the MDS plot.

The plotMDS() function from the limma package creates a temporary log fold change between pairs and plots samples on a two-dimensional scatterplot so that distances on the plot approximate the typical \(log_2\) fold changes between the samples.

A simplified example of how we calculate the logFC for our MDS plot.
  1. For a given counts table
sample_1 sample_2 sample_3
Gene_1 3.0 0.25 2.8
Gene_2 2.9 0.8 1.0
Gene_3 2.2 1.0 1.5

….

  1. We take our gene counts and calculate the logFC between each pair of genes
  • logFC Gene_1: sample_1 versus sample_2 \(= log(\frac{3}{.25}) = 3.58\)
  • logFC Gene_2: sample_1 versus sample_2 \(= log(\frac{2.9}{.8}) = 1.86\)
  • Continue calculating the logFC between each pair of genes
  1. Lastly, take the average of the absolute value of the logFC among genes
  • nb: we take the absolute value so the negative log fold changes don’t cancel out the positive ones
  1. We can now use these distances between samples to plot our MDS.

Thankfully we don’t need to perform all of these calculations by hand, or even write our own script to do so

plotMDS(cpm(dgeList_Filtered, log = TRUE))
title(main = "Multi Dimensional Scaling Plot \nMouse Chromosome 1")
MDS showing two clear groups in the data. limma::plotMDS

MDS showing two clear groups in the data. limma::plotMDS

Here we see that the Leading logFC on dimension 1 appears to seperate the samples by tissue type suggesting that as we set out to investigate - there is a difference in gene expression between cerebellar cortex (cbc) and skeletal muscle (skm).

WARNING: it is easy to observe the difference between our groups on the MDS plot and assign them directly to tissue type. The files we are using here are part of a much larger research effort and as such there is much information not being assessed. eg. were our samples all sequenced at the same time, on the same machine? The differences we observe could be due to something we call batch effects.

Batch effects can be defined as systematic non-biological variation between groups of samples (or batches) due to experimental artifacts. Batch effects are a complication of high-throughput studies which occur when measurements are affected by laboratory conditions. The topic of batch effects and how to account for them deserves its own session but we don’t have the time to delve into that here. If you are interested in reading more about batch effects and how to overcome them the rna-seqblog has a number of interesting articles discussing the topic.

We can use the plotMDS() function directly to create an MDS plot as we did above, or assign the data created to an object that we can pipe into ggplot for a presentation ready figure.

mds <- dgeList_Filtered %>%
  cpm(log = TRUE) %>%
  plotMDS()
yes

yes

The following chunk wraps the plotting of our MDS plot inside plotly::ggplotly() which takes a ggplot object and makes it interactive. You may notice that label was included as a plotting aesthetic, but no labels were added as a layer in ggplot2. These will instead be added once the plot is made interactive (try hovering the mouse over one of the points on the plot to see what we mean by interactive). Interactive plots will only be interactive in the Viewer tab or when compiling an RMarkdown document to an html format, and will remain static if rendering to an MS Word document or pdf.

plotly::ggplotly(
  as.data.frame(cbind(dim1=mds$x,dim2= mds$y)) %>%
        rownames_to_column("sample") %>%
        as_tibble() %>%
        left_join(rownames_to_column(dgeList_Filtered$samples, "sample")) %>%
        ggplot(aes(x = dim1, y = dim2,
                   colour = group,
                   label = sample)) +
    xlab("Leading logFC Dim 1") +
    ylab("Leading logFC Dim 2") +
        geom_point(size = 3) +
        theme_bw()
)

MDS showing two clear groups in the data

In the spirit of interactive plots available in R, run the following code chunk to create an interactive MDS plot as an HTML page using Glimma::glMDSPlot. If you set \(launch=TRUE\) this code chunk will open a new browser window containing an MDS plot in the left panel and a bar plot showing the proportion of variation explained by each dimension in the right panel.

dgeList_Filtered %>%
  cpm(log=TRUE) %>%
  glMDSPlot(., labels=rownames(dgeList_Filtered$samples),
            groups=dgeList_Filtered$samples$group,
            launch=TRUE)

Tasks

  1. Try clicking on the bars of the bar plot to change the pair of dimensions being plotted on the MDS.
  2. What might account for the variation between dimension 2 and dimension 3?

PCA

Next we might choose to perform a Principal Component Analysis on our data, commonly abbreviated to PCA. This time, let’s take our CPM values & asses them on the log2 scale to make sure our PCA results are not heavily skewed by highly expressed genes. Our first step is to create a new object to hold the PCA results. In the following code chunk we take our filtered DGEList obeject dgeList_Filtered and pipe it to the cpm() function we used earlier to calculate the CPM values for each gene prior to setting the filtering threshold. We have made one small change here by including \(log - TRUE\) in the function call. By default this is \(log_2\). Next we pipe the \(log2\)CPM values into the t() function. This will transpose the matrix of counts such that the matrix now has genes in the columns and samples in the rows, which we then pipe into prcomp from the stats package. prcomp() returns a list with class “prcomp” containing a number of useful components.

To find out more about prcomp try typing help("prcomp") into the console. This will open the R Documentation for this function in the Help tab.

pca <- dgeList_Filtered %>%
    cpm(log = TRUE) %>%
    t() %>%
    prcomp() 

In our DGEList, we have the genes as the variables of interest for our main analysis, however for the PCA we’re looking at out samples as the variables of interest. The third line in the above code chunk has transposed (t()) the matrix returned by cpm(log = TRUE) to place the samples as the rows, which is where the function prcomp() expects to see the variables of interest.

Run the next code chunk for a quick inspection of the results shows that the first two components capture most of the variability, as expected. Beyond this observation, the details of PCA are beyond what we can cover here.

summary(pca)$importance %>%
  pander(split.tables = Inf)
  PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
Standard deviation 52.73 15.1 12.08 11.1 8.862 8.448 7.239 1.786e-14
Proportion of Variance 0.799 0.0655 0.04194 0.03539 0.02257 0.02051 0.01506 0
Cumulative Proportion 0.799 0.8645 0.9065 0.9418 0.9644 0.9849 1 1

Just as we did with the MDS plot, we can plot our PCA results to see if samples group clearly with their tissue type based on our main two principal components (ie PC1 and PC2). Much the same as the MDS plot, any clear separation of our groups of interest can be considered a positive sign that we will find differentially expressed genes.

plotly::ggplotly(
    pca$x %>%
        as.data.frame() %>%
        rownames_to_column("sample") %>%
        as_tibble() %>%
        dplyr::select(sample, PC1, PC2) %>%
        left_join(rownames_to_column(dgeList_Filtered$samples, "sample")) %>%
        ggplot(aes(x = PC1, y = PC2,
                   colour = group,
                   label = sample)) +
        geom_point(size = 3) +
        theme_bw()
)

PCA showing two clear groups in the data

Multi-Dimensional Scaling can look similar to PCA, but asks a slightly different question of the data. PCA creates plots based on correlations between samples, MDS creates plots based on distances between samples (here logFC between genes). Both methods return the results: 1. Coordinates for a graph 2. Percent of variation each axis accounts for 3. Loading scores (to determine which variables have the largest effect)

It is common to perform one or both of these analyses before starting a differential expression analysis but rest assured that usually results found under both methods will reveal similar patterns in your data.

For a great tutorial on multi-dimensional scaling check out “StatQuest: MDS and PCoA” here, and a companion vidoe about PCA titled “StatQuest: Principal Component Analysis (PCA), Step-by-Step” here.

Tasks

  1. How much variation is accounted for in PC1 and PC2 combined?
  2. Using the code chunk above as a template, plot PC3 and PC4 using ggplot and wrapping with plotly::ggplotly to investigate other sources of variation.

Differential Expression

In the previous sections we have worked with read counts, which are a discrete value and formally cannot be modelled using the assumption of normally distributed data (recall that counts data typically approximate a negative binomial distribution, not a normal distribution). The failure to meet the assumption of normality rules out linear models and t-tests for RNA-Seq, so many packages have been developed which use the negative binomial distribution to model these counts (e.g. edgeR and DESeq2).

An alternative was proposed by Law et al., where they apply a system of weights to the counts which allow the assumption of normality to be applied. This method is called voom and we’ll use this today.

Voom transformation and calculation of variance weights

First, lets set up a model matrix to specify the model to be fitted.

model_matrix <- model.matrix(~0 + group, data = dgeList_Filtered$samples)

The above specifies a model where each coefficient corresponds to a group mean. Importantly we have used the samples elemenet of the DGEList object to set the model matrix. By using the DGEList object we ensure that the samples (and therefore the group assignment) has been given in the same order as the counts table.

Tasks

  1. Take a moment to inspect the model_matrix object. You can do this by simply typing the object name into the console and hitting the Enter key.
  2. What information has been added here?
  3. What could happen if the group assignment wasn’t given in the same order as the counts table?

Next we will

voomData <- voom(dgeList_Filtered, model_matrix, plot = TRUE)
yes

yes

Note that running the voom() function without the model_matrix object will add a design matrix to the data based on the group column of the DGEList object but which we chose to specify explicitly. This is something of a style choice, but either way we can now use the voom object to perform a simple linear regression on each gene, which amounts to a t-test in this dataset.

From here it’s a simple matter to code the analysis and inspect results.

A lot of analysis is performed in the following code chunk:

  1. We’ll use lmFit() to fit the model for every gene using the design matrix in voomData. Note that we’re not using CPM, but are instead fitting the counts directly, after incorporation of the voom-derived weights.
  2. Next we moderate the variances using an empirical Bayes approach. This uses the assumption that our variance estimates will be a mix of overestimates and underestimates, and shrinks them all towards a central value. As a strategy, this is widely accepted and has been shown to increase power and reduce false positives.
  3. Finally, we’ll create a list of all genes with a summary of the results which we then convert to a tibble. The last step will remove the gene IDs (i.e. the row names), but because we’ve included our gene information in the DGEList object, this will be added to the results and we’ll still know which gene is which (now we see why we needed to ensure the genesGR object was added in the same order as the countData object).
topTable <- voomData %>% 
    lmFit() %>%
    eBayes %>%
    topTable(coef = "groupskm", n = Inf) %>%
    as_tibble()

Recall from our model_matrix that we are comparing groupcbc with groupskm (we could rename these if we wanted to but for our purpose here we can leave them as is). This means that when we call the coefficient groupskm in the topTable function we are using the column name to specify which coefficient or contrast of the linear model is of interest

Note that the GRanges information has been coerced into columns to form a data.frame/tibble and that looks a bit messy. To tidy things up, the following code will join all of the genomic co-ordinates into a single column, rename a few columns on the fly and by not asking for ID.width & ID.gene_biotype, we’ve saved ourselves dealing with a few redundant columns. We could actually add this to the chunk above to save forming then modifying an R object, but it’s been left separate for clarity.

Tasks

  1. Take a quick look at the topTable object before and after the tidy up by typing topTable %>% head() into the console and hitting the Enter key.
topTable <- topTable %>%
    unite("Range", ID.start, ID.end, sep = "-") %>%
    unite("Location", ID.seqnames, Range, ID.strand, sep = ":") %>%
    dplyr::select(Geneid = ID.gene_id, 
                  Symbol = ID.gene_name,
                  AveExpr, logFC, t, P.Value, 
                  FDR = adj.P.Val, 
                  Location, 
                  Entrez = ID.entrezid)

Now that we have our ranked list of genes, we should really inspect these visually. A commonly used plot is a volcano plot, where the position of values on the x-axis is the logFC estimates for each gene, and the position on the y-axis relates to the strength of statistical significance for each gene. Before plotting, we added a simple column called DE to indicate whether we considered a gene to be differentially expressed (ie. the mean between the groups is different), based purely on an FDR-adjusted p-value < 0.05. To add some colour to the plot (and to make identifying the DE genes easy) we can use this DE status to colour points.

topTable %>%
    mutate(DE = FDR < 0.05) %>%
    ggplot(aes(logFC, -log10(P.Value), colour = DE)) +
    geom_point(alpha = 0.5) +
    # geom_text_repel(data = . %>%
    #                     dplyr::filter(DE) %>%
    #                     dplyr::filter(-log10(P.Value) > 4 | abs(logFC) > 2.5),
    #                 aes(label = Symbol)) +
    # scale_colour_manual(values = c("grey", "red")) +
    theme_bw() +
    theme(legend.position = "none")
Volcano plot showing DE genes between the two tissue types.

Volcano plot showing DE genes between the two tissue types.

So what have we done here. Firstly we took our topTable object that contains the logFC values and FDR for each gene and piped the object into mutate. Here we have used mutate to create a new column called DE based on the logical question is the value in the FDR column less than 0.05?. For each row in our table if the value of the FDR column is < 0.05 a TRUE value will be returned, and of course a value of FALSE if \(\geq\) 0.05. As another perspective, we might like to see whether these genes are at the high or low end of the range for expression values. This is often referred to as an MD plot, which stands for Mean expression vs Difference, where difference is more commonly referred to as logFC. We’ve added an arrange() call in the following to ensure our DE genes are plotted last and are the most visible in our figure.

topTable %>%
    mutate(DE = FDR < 0.05) %>%
    arrange(desc(P.Value)) %>%
    ggplot(aes(AveExpr, logFC, colour = DE)) +
    geom_point(alpha = 0.5) +
    geom_text_repel(data = . %>%
                        dplyr::filter(DE) %>%
                        dplyr::filter(abs(logFC) > 2 | AveExpr > 14),
                    aes(label = Symbol)) +
    scale_colour_manual(values = c("grey", "red")) +
    labs(x = "Average Expression (log2 CPM)",
         y = "log Fold-Change") +
    theme_bw() +
    theme(legend.position = "none")
Mean-Difference plot showing fold-change potted against expression level. Genes considered as DE are highlighted in red.

Mean-Difference plot showing fold-change potted against expression level. Genes considered as DE are highlighted in red.

If were happy with our results, we could exprt this list using write_csv() or write_tsv(). We could also produce a short table summarising the “big guns” in the dataset. As there are more than 100 genes considered as DE here, let’s restrict to those with logFC beyond the range \(\pm1\), which equates to 2-fold up or down-regulation.

topTable %>%
    dplyr::filter(FDR < 0.05, abs(logFC) > 1) %>%
    dplyr::select(ID = Geneid, Symbol, AveExpr, logFC, P.Value, FDR) %>%
    pander(caption = paste("The", nrow(.), "most DE genes when ranked by p-value, and filtered on a logFC beyond the range $\\cpm1$"))
The 1111 most DE genes when ranked by p-value, and filtered on a logFC beyond the range \(\cpm1\)
ID Symbol AveExpr logFC P.Value FDR
ENSMUSG00000034066 Farp2 12.8 13.78 6.233e-21 1.774e-18
ENSMUSG00000033021 Gmppa 11.62 16.6 1.479e-20 1.774e-18
ENSMUSG00000088669 Gm25336 14.83 15.22 1.368e-20 1.774e-18
ENSMUSG00000099382 Gm28631 13.7 14.05 9.073e-21 1.774e-18
ENSMUSG00000102278 Gm37145 14.99 15.4 3.241e-20 2.412e-18
ENSMUSG00000103617 Gm37950 12.12 12.86 9.966e-21 1.774e-18
ENSMUSG00000102276 2900069G24Rik 11.74 12.44 7.428e-21 1.774e-18
ENSMUSG00000064823 Snord82 12.16 12.38 1.205e-20 1.774e-18
ENSMUSG00000087230 Mroh3 9.395 14.99 5.368e-20 3.322e-18
ENSMUSG00000026429 Ube2t 11.83 13.15 2.353e-20 2.383e-18
ENSMUSG00000103152 Gm8790 11.02 12.29 1.572e-20 1.774e-18
ENSMUSG00000026117 Zap70 11.88 12.04 1.385e-20 1.774e-18
ENSMUSG00000103799 Gm38093 11.75 12.82 2.77e-20 2.412e-18
ENSMUSG00000099622 Gm29188 11.57 11.94 1.592e-20 1.774e-18
ENSMUSG00000064612 Gm26457 12.1 13.01 4.212e-20 2.933e-18
ENSMUSG00000043716 Rpl7 11.62 12.29 2.897e-20 2.412e-18
ENSMUSG00000103763 Gm37860 11.23 12.81 4.954e-20 3.246e-18
ENSMUSG00000049608 Gpr55 12.25 13.05 6.538e-20 3.833e-18
ENSMUSG00000060985 Tdrd5 10.73 11.94 3.248e-20 2.412e-18
ENSMUSG00000077202 Gm25612 11.64 13.04 7.764e-20 4.048e-18
ENSMUSG00000042849 Olfr1414 11.71 12.56 7.055e-20 3.93e-18
ENSMUSG00000100185 Gm28634 7.863 13.04 1.013e-19 4.386e-18
ENSMUSG00000026220 Slc16a14 12.14 12.59 7.995e-20 4.048e-18
ENSMUSG00000016526 Dyrk3 10.82 12.57 8.861e-20 4.121e-18
ENSMUSG00000094638 Gm21972 11.55 12.35 1.156e-19 4.474e-18
ENSMUSG00000025980 Hspd1 12.19 12.12 1.024e-19 4.386e-18
ENSMUSG00000025907 Rb1cc1 11.27 11.84 8.879e-20 4.121e-18
ENSMUSG00000102605 Gm37264 12.77 14.29 2.971e-19 7.195e-18
ENSMUSG00000077794 Gm25560 11.13 12.21 1.267e-19 4.704e-18
ENSMUSG00000026121 Sema4c 8.491 12.93 2.137e-19 5.806e-18
ENSMUSG00000026554 Dcaf8 12.01 12.08 1.713e-19 5.742e-18
ENSMUSG00000090637 Gm6189 12.04 12.95 2.854e-19 7.064e-18
ENSMUSG00000051285 Pcmtd1 11.1 11.36 1.086e-19 4.474e-18
ENSMUSG00000089990 Gm15852 11.42 12.35 2.423e-19 6.331e-18
ENSMUSG00000104022 Gm37214 10.87 11.29 1.165e-19 4.474e-18
ENSMUSG00000026484 Rnf2 11.74 12.37 2.54e-19 6.43e-18
ENSMUSG00000026201 Stk16 10.99 11.61 1.752e-19 5.742e-18
ENSMUSG00000101840 Gm28294 13.5 14.68 6.436e-19 1.144e-17
ENSMUSG00000061816 Myl1 10.77 12.54 3.398e-19 7.885e-18
ENSMUSG00000087820 Mir1928 12.54 14.29 6.586e-19 1.146e-17
ENSMUSG00000026494 Kif26b 12.65 12.34 3.493e-19 7.942e-18
ENSMUSG00000025917 Cops5 10.33 11.3 2.11e-19 5.806e-18
ENSMUSG00000005681 Apoa2 11.18 12.34 4.354e-19 9.327e-18
ENSMUSG00000103227 Gm37733 9.869 11.08 1.895e-19 5.806e-18
ENSMUSG00000006301 Tmbim1 10.68 10.89 1.704e-19 5.742e-18
ENSMUSG00000103705 4930518J20Rik 10.22 10.71 1.434e-19 5.153e-18
ENSMUSG00000064435 Gm25588 10.48 11.73 3.855e-19 8.421e-18
ENSMUSG00000091844 Gm8251 10.47 10.9 2.075e-19 5.806e-18
ENSMUSG00000026037 Orc2 10.79 10.65 1.818e-19 5.787e-18
ENSMUSG00000102716 Gm5099 11.17 11.89 5.271e-19 1.068e-17
ENSMUSG00000088432 Gm25384 9.929 10.76 2.444e-19 6.331e-18
ENSMUSG00000037470 Uggt1 10.35 10.55 2.043e-19 5.806e-18
ENSMUSG00000001143 Lman2l 10.71 10.82 3.041e-19 7.208e-18
ENSMUSG00000047067 Dusp28 11.74 12.92 1.058e-18 1.551e-17
ENSMUSG00000058603 Rpl28-ps1 10.52 10.46 2.118e-19 5.806e-18
ENSMUSG00000095576 Fmo6 11.6 11.11 4.619e-19 9.708e-18
ENSMUSG00000099472 Gm29539 9.791 11.31 6.346e-19 1.144e-17
ENSMUSG00000102246 9430037O13Rik 10.32 11.18 5.859e-19 1.111e-17
ENSMUSG00000064602 Snora41 10.86 11.49 8.431e-19 1.317e-17
ENSMUSG00000104376 Gm37516 11.02 11.04 6.472e-19 1.144e-17
ENSMUSG00000067851 Arfgef1 11.63 12.27 1.318e-18 1.813e-17
ENSMUSG00000103978 4930474B08Rik 10.48 11 7.128e-19 1.203e-17
ENSMUSG00000092085 Gm17224 10.66 11.24 8.621e-19 1.317e-17
ENSMUSG00000073725 Lmbrd1 10.99 10.57 4.777e-19 9.855e-18
ENSMUSG00000102860 Gm37638 11.1 11.11 8.633e-19 1.317e-17
ENSMUSG00000103948 4930594C11Rik 11.15 11.98 1.423e-18 1.887e-17
ENSMUSG00000026179 Pnkd 10.45 10.6 5.741e-19 1.111e-17
ENSMUSG00000002881 Nab1 11.59 10.85 7.918e-19 1.26e-17
ENSMUSG00000101952 Gm10550 10.79 10.54 6.211e-19 1.144e-17
ENSMUSG00000026098 Pms1 7.204 10.91 8.928e-19 1.344e-17
ENSMUSG00000065629 Gm24826 10.64 11.26 1.185e-18 1.695e-17
ENSMUSG00000010609 Psen2 11.42 10.12 3.577e-19 7.969e-18
ENSMUSG00000005501 Usp40 10.99 10.58 6.899e-19 1.182e-17
ENSMUSG00000104306 Gm36972 8.918 11.78 1.593e-18 2.054e-17
ENSMUSG00000102404 5530400K19Rik 10.79 10.36 5.437e-19 1.082e-17
ENSMUSG00000088196 Gm23902 10.69 11.31 1.299e-18 1.809e-17
ENSMUSG00000037447 Arid5a 10.72 11.19 1.202e-18 1.695e-17
ENSMUSG00000026582 Sele 11.09 10.38 5.884e-19 1.111e-17
ENSMUSG00000102833 Gm9694 10.57 10.63 7.836e-19 1.26e-17
ENSMUSG00000099969 Gm28960 10.86 10.79 1.022e-18 1.518e-17
ENSMUSG00000094394 Gm23402 10.55 11.19 1.406e-18 1.887e-17
ENSMUSG00000103744 Gm38238 10.21 10.37 7.593e-19 1.26e-17
ENSMUSG00000100021 Gm18697 10.45 11.35 1.785e-18 2.26e-17
ENSMUSG00000052534 Pbx1 10.43 11.35 1.947e-18 2.41e-17
ENSMUSG00000025903 Lypla1 9.673 10.65 1.193e-18 1.695e-17
ENSMUSG00000099771 Gm19257 11.05 11.13 1.926e-18 2.41e-17
ENSMUSG00000100620 Gm28277 10.57 11.07 2.08e-18 2.519e-17
ENSMUSG00000087886 Gm23599 10.42 11.48 2.807e-18 3.191e-17
ENSMUSG00000085857 Gm15392 10.02 10.44 1.357e-18 1.843e-17
ENSMUSG00000096968 Gm26620 10.02 9.964 7.875e-19 1.26e-17
ENSMUSG00000026617 Bpnt1 10.27 11.02 2.443e-18 2.899e-17
ENSMUSG00000046994 Mars2 10.01 10.47 1.604e-18 2.054e-17
ENSMUSG00000103644 Gm29678 9.729 10.71 2.592e-18 3.04e-17
ENSMUSG00000014980 Tsen15 10.76 11.03 3.263e-18 3.531e-17
ENSMUSG00000026499 Acbd3 10.41 10.83 2.921e-18 3.256e-17
ENSMUSG00000113084 A030003K21Rik 10.29 10.09 1.477e-18 1.935e-17
ENSMUSG00000100770 Gm28466 11.02 10.7 2.792e-18 3.191e-17
ENSMUSG00000026085 Lyg1 9.512 10.61 2.705e-18 3.139e-17
ENSMUSG00000103716 Gm19430 11.31 10.3 2.072e-18 2.519e-17
ENSMUSG00000103696 Gm37531 11.8 12.04 6.474e-18 5.961e-17
ENSMUSG00000079658 Eloc 10.39 10.61 3.265e-18 3.531e-17
ENSMUSG00000018189 Uchl5 10.01 10.22 2.446e-18 2.899e-17
ENSMUSG00000101409 1700122D07Rik 10.04 10.94 5.042e-18 4.927e-17
ENSMUSG00000104418 Gm37070 10.33 10.35 3.666e-18 3.853e-17
ENSMUSG00000073494 Sh2d1b2 9.553 10.11 2.923e-18 3.256e-17
ENSMUSG00000026107 Nabp1 8.73 10.13 3.121e-18 3.443e-17
ENSMUSG00000072295 C2cd6 11.56 11.68 8.641e-18 7.511e-17
ENSMUSG00000012187 Mogat1 9.785 10.3 4.423e-18 4.438e-17
ENSMUSG00000042686 Jph1 11.18 13.54 1.557e-17 1.186e-16
ENSMUSG00000062963 Ufc1 12.47 13.63 1.646e-17 1.23e-16
ENSMUSG00000077257 Gm26080 10.11 9.99 3.926e-18 4.088e-17
ENSMUSG00000102784 Gm38251 9.294 10.03 4.28e-18 4.335e-17
ENSMUSG00000026182 Tnp1 10.24 10.82 8.108e-18 7.112e-17
ENSMUSG00000103636 Gm38331 10.24 10.28 5.66e-18 5.389e-17
ENSMUSG00000048874 Phf3 10.66 10.69 7.68e-18 6.79e-17
ENSMUSG00000026177 Slc11a1 10.33 10.99 9.263e-18 7.809e-17
ENSMUSG00000026258 3110079O15Rik 10.36 10.37 6.347e-18 5.91e-17
ENSMUSG00000042229 Rabif 9.478 10.95 9.37e-18 7.809e-17
ENSMUSG00000025940 Tmem70 9.437 10.29 6.354e-18 5.91e-17
ENSMUSG00000089803 Gm10171 9.856 9.885 4.196e-18 4.318e-17
ENSMUSG00000086053 Gm15178 9.246 9.745 3.568e-18 3.785e-17
ENSMUSG00000099719 Gm28802 9.188 9.713 3.467e-18 3.714e-17
ENSMUSG00000099760 Gm28800 10.69 10.37 7.343e-18 6.544e-17
ENSMUSG00000104368 2010103J01Rik 10.04 9.807 4.225e-18 4.318e-17
ENSMUSG00000046062 Ppp1r15b 10.66 10.23 6.807e-18 6.216e-17
ENSMUSG00000102380 Gm38140 9.725 10.56 9.149e-18 7.78e-17
ENSMUSG00000103497 Gm37407 11.36 11.67 1.53e-17 1.186e-16
ENSMUSG00000040485 Lrrc52 9.486 9.784 4.569e-18 4.544e-17
ENSMUSG00000103569 Gm38067 10.48 10.4 9.793e-18 8.025e-17
ENSMUSG00000005886 Ncoa2 10.51 10.01 7.087e-18 6.419e-17
ENSMUSG00000088951 Gm24940 8.742 10.19 8.711e-18 7.511e-17
ENSMUSG00000103804 Gm37062 9.351 9.664 4.835e-18 4.766e-17
ENSMUSG00000103180 Gm37089 10.96 11.11 1.559e-17 1.186e-16
ENSMUSG00000025938 Slco5a1 8.16 9.687 5.233e-18 5.07e-17
ENSMUSG00000048000 Gigyf2 10.15 10.57 1.259e-17 9.946e-17
ENSMUSG00000101911 Gm28381 9.477 9.657 5.544e-18 5.324e-17
ENSMUSG00000102234 Gm37885 9.785 9.702 6.366e-18 5.91e-17
ENSMUSG00000033845 Mrpl15 9.405 10.39 1.237e-17 9.84e-17
ENSMUSG00000026213 Stk11ip 9.938 11.11 1.923e-17 1.387e-16
ENSMUSG00000103159 F830112A20Rik 10.58 10.55 1.565e-17 1.186e-16
ENSMUSG00000046057 Gm15428 9.937 9.773 8.765e-18 7.511e-17
ENSMUSG00000102474 2610012C04Rik 10.77 11.83 2.845e-17 1.883e-16
ENSMUSG00000103760 Gm8009 11.16 10.26 1.542e-17 1.186e-16
ENSMUSG00000100556 Gm28902 10.89 11.21 2.523e-17 1.746e-16
ENSMUSG00000066595 Flvcr1 9.612 9.776 1.037e-17 8.428e-17
ENSMUSG00000025979 Mob4 11.35 11.95 3.313e-17 2.074e-16
ENSMUSG00000045210 Vcpip1 9.961 9.629 9.393e-18 7.809e-17
ENSMUSG00000026343 Gpr39 10.03 9.585 9.797e-18 8.025e-17
ENSMUSG00000026500 Cox20 9.668 10.27 1.93e-17 1.387e-16
ENSMUSG00000026112 Coa5 10.7 9.643 1.092e-17 8.815e-17
ENSMUSG00000026413 Pkp1 9.7 11.07 3.093e-17 1.958e-16
ENSMUSG00000067106 Gm5529 9.51 9.298 7.145e-18 6.419e-17
ENSMUSG00000103222 Gm37729 10.54 10.83 2.994e-17 1.949e-16
ENSMUSG00000103197 Gm37642 9.652 10.35 2.382e-17 1.669e-16
ENSMUSG00000033544 Angptl1 10.39 10.49 2.711e-17 1.837e-16
ENSMUSG00000026100 Mstn 8.781 9.917 1.969e-17 1.406e-16
ENSMUSG00000100389 Rbm6-ps2 9.186 9.376 1.16e-17 9.295e-17
ENSMUSG00000010290 AI597479 9.764 9.565 1.583e-17 1.191e-16
ENSMUSG00000103979 Gm20305 9.292 9.669 1.798e-17 1.309e-16
ENSMUSG00000026435 Slc45a3 10.45 10.42 3.563e-17 2.169e-16
ENSMUSG00000103215 Gm38388 9.857 9.985 2.694e-17 1.837e-16
ENSMUSG00000104524 Gm37333 10.92 10.27 3.645e-17 2.177e-16
ENSMUSG00000094131 Gm22265 9.131 9.409 1.712e-17 1.263e-16
ENSMUSG00000093650 Gm20631 9.723 9.861 3.009e-17 1.949e-16
ENSMUSG00000096297 Gm23934 10.78 10.67 4.98e-17 2.802e-16
ENSMUSG00000081429 Rps27-ps1 9.878 9.721 2.771e-17 1.849e-16
ENSMUSG00000041075 Fzd7 10.68 9.766 2.906e-17 1.905e-16
ENSMUSG00000026490 Cdc42bpa 9.994 10.09 3.802e-17 2.241e-16
ENSMUSG00000089822 Gm15759 8.938 9.907 3.383e-17 2.105e-16
ENSMUSG00000094245 Gm23966 11.09 10.93 5.829e-17 3.23e-16
ENSMUSG00000077220 Snord78 9.704 9.86 3.452e-17 2.136e-16
ENSMUSG00000096117 Gm26271 9.194 9.234 1.71e-17 1.263e-16
ENSMUSG00000044340 Phlpp1 9.325 9.405 2.202e-17 1.552e-16
ENSMUSG00000102558 Gm37571 9.355 9.629 2.857e-17 1.883e-16
ENSMUSG00000110596 Gm45859 9.534 9.397 2.171e-17 1.54e-16
ENSMUSG00000102157 Gm37470 9.199 9.693 3.138e-17 1.975e-16
ENSMUSG00000099519 Gm29253 9.249 9.531 2.732e-17 1.837e-16
ENSMUSG00000036907 C1ql2 7.741 11.58 8.162e-17 4.229e-16
ENSMUSG00000073485 H3f3aos 9.198 9.043 1.39e-17 1.091e-16
ENSMUSG00000026782 Abi2 9.754 9.589 3.082e-17 1.958e-16
ENSMUSG00000044689 Gm13749 8.761 9.125 1.784e-17 1.308e-16
ENSMUSG00000026153 Fam135a 9.669 9.478 3.054e-17 1.958e-16
ENSMUSG00000099959 Gm29487 10.51 9.352 2.66e-17 1.829e-16
ENSMUSG00000100319 Gm5256 9.046 9.595 3.589e-17 2.173e-16
ENSMUSG00000048234 Rnf149 9.075 9.463 3.075e-17 1.958e-16
ENSMUSG00000038855 Itpkb 11.49 13.76 1.216e-16 5.765e-16
ENSMUSG00000099753 Gm28058 8.525 9.24 2.399e-17 1.67e-16
ENSMUSG00000090322 Gm17090 9.707 10.18 6.091e-17 3.342e-16
ENSMUSG00000025991 Cps1 9.023 16.75 1.421e-16 6.461e-16
ENSMUSG00000070738 Dgkd 9.457 9.281 2.737e-17 1.837e-16
ENSMUSG00000034343 Ube2f 10.17 11.28 9.494e-17 4.829e-16
ENSMUSG00000048865 Arhgap30 9.793 9.968 5.61e-17 3.125e-16
ENSMUSG00000103226 Gm37732 8.842 9.61 4.179e-17 2.412e-16
ENSMUSG00000101746 2310043L19Rik 11.95 11.4 1.009e-16 5.039e-16
ENSMUSG00000025937 Lactb2 8.637 9.483 3.835e-17 2.248e-16
ENSMUSG00000026437 Cdk18 9.411 9.617 4.729e-17 2.688e-16
ENSMUSG00000077616 Gm25502 9.812 11.13 1.062e-16 5.233e-16
ENSMUSG00000038034 Igsf8 11.01 9.321 3.61e-17 2.174e-16
ENSMUSG00000101265 Gm29099 10.03 10.2 7.726e-17 4.041e-16
ENSMUSG00000103966 Gm37120 9.208 9.871 6.422e-17 3.473e-16
ENSMUSG00000100966 Gm5700 8.932 9.208 3.554e-17 2.169e-16
ENSMUSG00000025920 Stau2 10.83 10.94 1.138e-16 5.488e-16
ENSMUSG00000044306 4930500M09Rik 8.8 9.943 7.656e-17 4.039e-16
ENSMUSG00000049690 Nckap5 8.533 9.72 6.519e-17 3.508e-16
ENSMUSG00000026020 Nop58 9.142 9.22 3.766e-17 2.232e-16
ENSMUSG00000051081 Gm4847 10.09 9.426 5.064e-17 2.835e-16
ENSMUSG00000053318 Slamf8 8.149 9.238 4.148e-17 2.407e-16
ENSMUSG00000104237 Gm33533 11.31 11.97 1.553e-16 6.977e-16
ENSMUSG00000104212 Gm37986 9.281 9.083 3.654e-17 2.177e-16
ENSMUSG00000034107 Ano7 9.874 9.27 4.698e-17 2.684e-16
ENSMUSG00000073609 D2hgdh 9.49 9.123 4.476e-17 2.57e-16
ENSMUSG00000026514 Cnih3 9.465 9.888 9.942e-17 5.011e-16
ENSMUSG00000088911 Gm22609 9.371 8.995 3.943e-17 2.3e-16
ENSMUSG00000070182 Gm24817 7.739 9.325 6.256e-17 3.412e-16
ENSMUSG00000046367 Mgat4e 9.096 9.084 4.825e-17 2.729e-16
ENSMUSG00000104210 Gm37984 10.11 9.47 7.687e-17 4.039e-16
ENSMUSG00000104341 Gm37688 8.984 9.382 7.12e-17 3.795e-16
ENSMUSG00000103182 Gm37091 9.015 9.808 1.039e-16 5.167e-16
ENSMUSG00000097899 Gm16894 10.33 9.584 8.911e-17 4.575e-16
ENSMUSG00000067071 Hes6 7.155 12.19 2.147e-16 9.128e-16
ENSMUSG00000037138 Aff3 9.071 9.217 6.279e-17 3.412e-16
ENSMUSG00000104297 Gm38046 8.657 9.285 7.022e-17 3.761e-16
ENSMUSG00000103289 Gm31256 9.207 10.02 1.287e-16 6.049e-16
ENSMUSG00000026032 Ndufb3 8.688 8.794 3.511e-17 2.161e-16
ENSMUSG00000115958 Kiss1 9.761 9.688 1.125e-16 5.45e-16
ENSMUSG00000048174 Tmem81 11.17 10.59 1.746e-16 7.597e-16
ENSMUSG00000026491 Ahctf1 8.024 9.088 6.026e-17 3.323e-16
ENSMUSG00000104427 Gm38368 8.981 9.439 9.059e-17 4.629e-16
ENSMUSG00000099576 Gm29040 9.278 9.505 1.076e-16 5.282e-16
ENSMUSG00000028033 Kcnq5 8.759 10.33 1.809e-16 7.84e-16
ENSMUSG00000101275 1700012E03Rik 10.53 10.45 2.026e-16 8.714e-16
ENSMUSG00000103522 9430087J23Rik 10.03 10.46 2.136e-16 9.128e-16
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ENSMUSG00000048775 Serpinb13 9.348 9.137 1.044e-16 5.168e-16
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ENSMUSG00000103317 Gm38359 8.512 8.902 7.793e-17 4.057e-16
ENSMUSG00000052428 Tmco1 9.014 9.251 1.254e-16 5.919e-16
ENSMUSG00000033793 Atp6v1h 9.903 9.296 1.338e-16 6.186e-16
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ENSMUSG00000006411 Nectin4 8.898 9.174 1.155e-16 5.545e-16
ENSMUSG00000103383 Gm37754 12.99 11.59 3.402e-16 1.404e-15
ENSMUSG00000047528 Als2cr12 9.754 10.44 2.646e-16 1.117e-15
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ENSMUSG00000089088 Gm24001 8.829 9.159 1.207e-16 5.748e-16
ENSMUSG00000057329 Bcl2 10.17 9.342 1.58e-16 7.067e-16
ENSMUSG00000100069 Gm6801 7.25 9.054 1.196e-16 5.719e-16
ENSMUSG00000061024 Rrs1 8.437 9.308 1.637e-16 7.293e-16
ENSMUSG00000082057 Gm15789 8.4 8.878 1.002e-16 5.027e-16
ENSMUSG00000102027 Gm28914 9.034 9.177 1.427e-16 6.464e-16
ENSMUSG00000026622 Nek2 11.25 10.51 3.521e-16 1.442e-15
ENSMUSG00000098259 Gm27616 9.398 8.767 9.805e-17 4.965e-16
ENSMUSG00000102722 Gm37935 9.399 8.97 1.321e-16 6.16e-16
ENSMUSG00000033227 Wnt6 9.354 9.183 1.686e-16 7.422e-16
ENSMUSG00000101130 Gm19637 9.044 8.803 1.121e-16 5.45e-16
ENSMUSG00000090277 Gm8338 10.57 9.777 2.835e-16 1.192e-15
ENSMUSG00000102383 Gm38142 8.842 9.134 1.738e-16 7.597e-16
ENSMUSG00000100981 Gm29072 8.801 9.074 1.644e-16 7.296e-16
ENSMUSG00000104360 Gm8762 9.12 8.855 1.328e-16 6.162e-16
ENSMUSG00000055067 Smyd3 8.556 8.872 1.356e-16 6.221e-16
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ENSMUSG00000098549 Gm5269 8.62 8.883 1.743e-16 7.597e-16
ENSMUSG00000103609 Gm37022 8.623 8.648 1.357e-16 6.221e-16
ENSMUSG00000101413 Gm29485 10.32 8.944 2.33e-16 9.87e-16
ENSMUSG00000026307 Scly 8.758 8.862 2.146e-16 9.128e-16
ENSMUSG00000084749 Gm25288 10.06 9.105 2.91e-16 1.219e-15
ENSMUSG00000101693 Gm19461 9.167 9.233 3.75e-16 1.519e-15
ENSMUSG00000006542 Prkag3 8.74 8.485 1.68e-16 7.422e-16
ENSMUSG00000103597 Gm19777 9.828 9.184 3.909e-16 1.566e-15
ENSMUSG00000058665 En1 7.301 9.235 4.089e-16 1.627e-15
ENSMUSG00000026442 Nfasc 9.665 8.988 3.459e-16 1.422e-15
ENSMUSG00000101189 1700029M03Rik 5.883 10.36 7.537e-16 2.827e-15
ENSMUSG00000103576 Gm38188 9.021 9.655 5.91e-16 2.278e-15
ENSMUSG00000053461 Hhipl2 8.932 8.839 3.315e-16 1.378e-15
ENSMUSG00000078190 Dnm3os 8.273 8.686 3.051e-16 1.273e-15
ENSMUSG00000089204 Mir1927 8.655 9.255 5.372e-16 2.093e-15
ENSMUSG00000010311 Optc 9.736 9.398 6.114e-16 2.349e-15
ENSMUSG00000100786 Gm28940 9.206 9.64 7.396e-16 2.793e-15
ENSMUSG00000082473 Kif22-ps 9.708 8.829 3.928e-16 1.568e-15
ENSMUSG00000103393 Gm38304 9.062 8.762 3.876e-16 1.564e-15
ENSMUSG00000013997 Nit1 9.452 9.981 8.835e-16 3.206e-15
ENSMUSG00000089299 Gm24637 9.005 9.044 5.297e-16 2.07e-15
ENSMUSG00000025911 Adhfe1 8.196 9.807 8.938e-16 3.233e-15
ENSMUSG00000026172 Bcs1l 8.691 8.608 3.583e-16 1.462e-15
ENSMUSG00000102752 Gm7694 10.99 9.627 8.482e-16 3.108e-15
ENSMUSG00000103400 Gm15853 6.778 10.98 1.239e-15 4.301e-15
ENSMUSG00000081130 Gm8217 9.105 8.895 5.263e-16 2.064e-15
ENSMUSG00000103467 Gm38245 8.518 9.477 8.558e-16 3.126e-15
ENSMUSG00000026360 Rgs2 9.672 9.558 8.969e-16 3.234e-15
ENSMUSG00000026283 Ing5 8.181 8.903 5.603e-16 2.167e-15
ENSMUSG00000102685 Gm37373 8.273 8.655 4.3e-16 1.705e-15
ENSMUSG00000109510 Gm42417 9.816 9.625 9.682e-16 3.446e-15
ENSMUSG00000106081 Gm47280 9.183 9.249 7.704e-16 2.862e-15
ENSMUSG00000102824 Pdcd5-ps 9.413 8.496 3.748e-16 1.519e-15
ENSMUSG00000038793 Lefty1 8.361 8.492 3.888e-16 1.564e-15
ENSMUSG00000087213 2810408I11Rik 9.515 9.032 6.975e-16 2.643e-15
ENSMUSG00000103867 9630010A21Rik 8.577 9.841 1.117e-15 3.924e-15
ENSMUSG00000007097 Atp1a2 10.11 15.69 1.892e-15 6.31e-15
ENSMUSG00000102690 Gm37779 9.204 8.259 3.333e-16 1.38e-15
ENSMUSG00000042451 Mybph 9.045 10.63 1.605e-15 5.452e-15
ENSMUSG00000103964 Gm8407 8.971 9.279 9.881e-16 3.506e-15
ENSMUSG00000033124 Atg9a 10.1 8.761 6.835e-16 2.599e-15
ENSMUSG00000042182 Bend6 9.195 8.571 5.558e-16 2.157e-15
ENSMUSG00000033813 Tcea1 8.346 8.369 4.403e-16 1.739e-15
ENSMUSG00000077846 Gm22232 9.451 8.932 8.835e-16 3.206e-15
ENSMUSG00000049160 Tex50 8.065 8.4 4.928e-16 1.94e-15
ENSMUSG00000103961 Gm36388 8.74 8.74 7.65e-16 2.86e-15
ENSMUSG00000026469 Xpr1 7.763 8.898 9.167e-16 3.294e-15
ENSMUSG00000040225 Prrc2c 8.236 8.693 7.881e-16 2.907e-15
ENSMUSG00000100913 Gm8392 7.942 8.464 6.163e-16 2.359e-15
ENSMUSG00000100120 Gm553 9.033 8.458 6.674e-16 2.546e-15
ENSMUSG00000026211 Obsl1 9.642 9.445 1.473e-15 5.033e-15
ENSMUSG00000100516 Gm29332 8.948 8.912 1.089e-15 3.839e-15
ENSMUSG00000025955 Akr1cl 9.351 8.534 7.734e-16 2.862e-15
ENSMUSG00000038599 Capn8 9.115 8.713 9.583e-16 3.422e-15
ENSMUSG00000049881 2810025M15Rik 9.177 8.344 7.43e-16 2.796e-15
ENSMUSG00000094668 Gm24871 8.654 8.338 7.734e-16 2.862e-15
ENSMUSG00000100108 Gm28100 10.63 8.909 1.376e-15 4.73e-15
ENSMUSG00000102009 4933400F21Rik 10.68 9.299 1.847e-15 6.177e-15
ENSMUSG00000033276 Stk36 9.236 8.736 1.218e-15 4.239e-15
ENSMUSG00000104163 Gm19057 8.769 8.588 1.061e-15 3.753e-15
ENSMUSG00000098829 Mir6354 9.897 9.232 1.77e-15 5.938e-15
ENSMUSG00000067158 Col4a4 8.006 8.318 7.957e-16 2.925e-15
ENSMUSG00000089093 Snord11 9.082 8.782 1.315e-15 4.55e-15
ENSMUSG00000102858 Gm37086 9.677 9.74 2.364e-15 7.769e-15
ENSMUSG00000103403 Gm37931 10.6 9.485 2.451e-15 7.998e-15
ENSMUSG00000101414 Gm29101 8.864 8.686 1.634e-15 5.532e-15
ENSMUSG00000013973 Dedd 8.918 8.7 1.728e-15 5.833e-15
ENSMUSG00000110712 Gm39662 8.558 9.466 2.792e-15 8.964e-15
ENSMUSG00000087233 Gm43213 8.35 8.108 9.584e-16 3.422e-15
ENSMUSG00000104011 Gm32391 8.106 8.249 1.137e-15 3.984e-15
ENSMUSG00000026051 1500015O10Rik 8.186 8.464 1.526e-15 5.199e-15
ENSMUSG00000026564 Dusp27 8.885 8.236 1.213e-15 4.234e-15
ENSMUSG00000090165 Ugt1a10 8.827 8.73 2.064e-15 6.842e-15
ENSMUSG00000042305 Tmem183a 6.858 12.22 4.96e-15 1.536e-14
ENSMUSG00000089683 4930570N18Rik 8.26 8.16 1.349e-15 4.652e-15
ENSMUSG00000026095 Asnsd1 8.593 9.268 3.308e-15 1.053e-14
ENSMUSG00000103908 Gm37502 8.515 8.928 2.776e-15 8.936e-15
ENSMUSG00000101085 Gm8531 8.447 8.154 1.383e-15 4.741e-15
ENSMUSG00000026019 Wdr12 9.444 8.48 2.35e-15 7.746e-15
ENSMUSG00000026135 Zfp142 8.579 8.143 1.767e-15 5.938e-15
ENSMUSG00000026547 Tagln2 8.383 8.181 1.9e-15 6.319e-15
ENSMUSG00000064246 Chil1 10.01 11.26 6.381e-15 1.932e-14
ENSMUSG00000100696 Gm8419 8.459 8.759 3.292e-15 1.051e-14
ENSMUSG00000093538 Gm20711 8.893 8.456 2.649e-15 8.553e-15
ENSMUSG00000103983 Gm20045 8.52 8.19 2.489e-15 8.083e-15
ENSMUSG00000026204 Ptprn 8.82 8.568 3.571e-15 1.13e-14
ENSMUSG00000100361 Gm7063 7.864 9.223 5.293e-15 1.624e-14
ENSMUSG00000052423 B4galt3 7.98 8.118 2.409e-15 7.894e-15
ENSMUSG00000026614 Slc30a10 8.592 8.07 2.455e-15 7.998e-15
ENSMUSG00000102594 Gm38381 7.276 10.15 7.633e-15 2.268e-14
ENSMUSG00000101112 Gm29185 12.97 13.61 9.174e-15 2.689e-14
ENSMUSG00000026155 Smap1 8.393 7.978 2.283e-15 7.548e-15
ENSMUSG00000081214 Rpl35a-ps2 9.502 8.997 5.851e-15 1.781e-14
ENSMUSG00000102782 Gm37625 7.351 8.481 4.247e-15 1.329e-14
ENSMUSG00000100280 Gm28417 8.535 8.258 3.532e-15 1.121e-14
ENSMUSG00000042197 Zfp451 7.4 8.107 3.126e-15 1.001e-14
ENSMUSG00000102689 Gm37217 10.82 11.1 9.755e-15 2.852e-14
ENSMUSG00000102049 Zbed6 8.018 8.4 4.176e-15 1.311e-14
ENSMUSG00000095731 Gm25312 9.155 8.661 5.191e-15 1.597e-14
ENSMUSG00000058248 Kcnh1 4.914 8.329 4.027e-15 1.267e-14
ENSMUSG00000047330 Kcne4 10.37 9.425 8.389e-15 2.486e-14
ENSMUSG00000026576 Atp1b1 8.433 8.407 4.949e-15 1.536e-14
ENSMUSG00000038776 Ephx1 7.941 7.777 2.579e-15 8.353e-15
ENSMUSG00000101964 Gm28693 7.706 8.329 5.068e-15 1.564e-14
ENSMUSG00000041879 Ipo9 8.724 8.494 5.794e-15 1.768e-14
ENSMUSG00000059956 Serpinb12 8.779 8.424 5.702e-15 1.745e-14
ENSMUSG00000053153 Spag16 7.639 8.182 4.962e-15 1.536e-14
ENSMUSG00000104509 Gm33994 7.211 7.92 3.936e-15 1.242e-14
ENSMUSG00000026170 Cyp27a1 7.817 8.449 6.757e-15 2.04e-14
ENSMUSG00000041945 Mfsd9 7.995 10.24 1.408e-14 4.075e-14
ENSMUSG00000100702 Gm8976 8.601 8.327 7.006e-15 2.103e-14
ENSMUSG00000026278 Bok 7.704 8.278 7.024e-15 2.103e-14
ENSMUSG00000025935 Tram1 8.92 8.946 1.075e-14 3.128e-14
ENSMUSG00000093286 Gm22569 8.747 8.164 6.852e-15 2.063e-14
ENSMUSG00000026275 Ppp1r7 7.517 7.833 4.891e-15 1.526e-14
ENSMUSG00000026127 Imp4 7.795 8.194 7.224e-15 2.158e-14
ENSMUSG00000104087 Gm38277 6.797 9.179 1.482e-14 4.266e-14
ENSMUSG00000089706 B230216N24Rik 9.986 9.545 1.707e-14 4.888e-14
ENSMUSG00000102776 Gm38162 7.733 7.796 6.2e-15 1.882e-14
ENSMUSG00000103727 Gm37097 7.631 8.088 8.466e-15 2.502e-14
ENSMUSG00000070871 Ccnyl1 7.634 8.137 9.858e-15 2.875e-14
ENSMUSG00000053333 Dis3l2 7.983 7.981 8.805e-15 2.588e-14
ENSMUSG00000093956 Gm24497 8.618 8.944 1.712e-14 4.889e-14
ENSMUSG00000101395 Gm8241 8.129 7.91 8.746e-15 2.578e-14
ENSMUSG00000042554 Zp3r 7.834 9.37 2.041e-14 5.77e-14
ENSMUSG00000038936 Sccpdh 8.368 7.701 7.387e-15 2.2e-14
ENSMUSG00000099556 Gm28857 8.75 8.248 1.266e-14 3.672e-14
ENSMUSG00000098368 Gm27648 7.697 8.664 1.731e-14 4.932e-14
ENSMUSG00000102976 Zc3h11a 7.939 8.353 1.457e-14 4.206e-14
ENSMUSG00000040181 Fmo1 6.487 10.04 2.748e-14 7.615e-14
ENSMUSG00000101482 Gm28883 10.3 9.012 2.496e-14 7.003e-14
ENSMUSG00000103646 Gm37706 9.739 8.339 1.816e-14 5.16e-14
ENSMUSG00000098680 Mir6898 8.081 8.663 2.189e-14 6.173e-14
ENSMUSG00000090399 Gm38399 9.554 8.835 2.53e-14 7.081e-14
ENSMUSG00000102596 Gm20203 8.256 8.051 1.641e-14 4.712e-14
ENSMUSG00000073491 Ifi213 11.25 8.749 2.618e-14 7.292e-14
ENSMUSG00000100106 Gm28856 8.389 8.136 1.892e-14 5.364e-14
ENSMUSG00000082528 Gm15182 8.788 8.846 2.956e-14 8.036e-14
ENSMUSG00000061863 Gm6822 8.563 9.285 3.542e-14 9.44e-14
ENSMUSG00000100351 Gm7867 8.847 8.591 3.052e-14 8.256e-14
ENSMUSG00000071890 Mroh9 8.089 9.851 4.352e-14 1.143e-13
ENSMUSG00000047443 Erfe 8.109 8.009 2.388e-14 6.717e-14
ENSMUSG00000026202 Tuba4a 8.597 8.231 3.062e-14 8.26e-14
ENSMUSG00000116158 Kiss1 10.08 8.378 3.338e-14 8.918e-14
ENSMUSG00000103328 Gm37406 8.556 8.298 3.26e-14 8.729e-14
ENSMUSG00000100504 Gm28926 9.008 9.345 4.897e-14 1.277e-13
ENSMUSG00000082160 Gm11578 9.536 8.099 3.124e-14 8.385e-14
ENSMUSG00000051079 Rgs13 9.233 7.949 2.835e-14 7.799e-14
ENSMUSG00000104140 Gm37140 8.371 8.067 3.053e-14 8.256e-14
ENSMUSG00000101741 Gm28055 8.112 9.173 5.12e-14 1.333e-13
ENSMUSG00000038174 Fam126b 8.113 7.844 2.814e-14 7.77e-14
ENSMUSG00000103146 Gm37745 7.482 7.795 2.712e-14 7.534e-14
ENSMUSG00000025939 Ube2w 8.41 7.85 2.958e-14 8.036e-14
ENSMUSG00000099996 Gm29065 9.504 7.779 2.908e-14 7.96e-14
ENSMUSG00000090550 Prlh 8.334 10.2 6.396e-14 1.645e-13
ENSMUSG00000104026 Gm37212 9.521 8.032 3.733e-14 9.925e-14
ENSMUSG00000042207 Kdm5b 8.136 7.688 2.871e-14 7.877e-14
ENSMUSG00000089906 Gm6428 8.075 7.744 3.085e-14 8.301e-14
ENSMUSG00000100685 Gm28610 7.442 8.723 5.382e-14 1.394e-13
ENSMUSG00000026581 Sell 8.314 8.059 4.055e-14 1.073e-13
ENSMUSG00000026463 Atp2b4 7.469 7.521 2.568e-14 7.17e-14
ENSMUSG00000097540 1700037F24Rik 8.75 8.165 4.418e-14 1.158e-13
ENSMUSG00000097784 A430105J06Rik 8.407 9.225 6.676e-14 1.706e-13
ENSMUSG00000086721 Gm13750 8.259 7.443 2.818e-14 7.77e-14
ENSMUSG00000101731 3830432H09Rik 7.812 7.466 2.937e-14 8.02e-14
ENSMUSG00000026090 2010300C02Rik 8.575 7.733 3.958e-14 1.05e-13
ENSMUSG00000101660 Gm18666 7.781 7.872 4.661e-14 1.219e-13
ENSMUSG00000034292 Traf3ip1 9.439 9.084 9.591e-14 2.385e-13
ENSMUSG00000090682 Gm3852 7.829 9.263 1.028e-13 2.533e-13
ENSMUSG00000101656 Gm8004 7.799 7.634 5.26e-14 1.366e-13
ENSMUSG00000025902 Sox17 7.208 7.839 6.452e-14 1.656e-13
ENSMUSG00000091017 Fam71a 7.285 7.317 4.23e-14 1.117e-13
ENSMUSG00000026389 Steap3 4.58 8.155 8.068e-14 2.038e-13
ENSMUSG00000046337 Fam178b 7.425 7.322 4.304e-14 1.134e-13
ENSMUSG00000103670 Gm37622 8.182 8.734 1.022e-13 2.53e-13
ENSMUSG00000104461 Gm37662 8.036 8.937 1.089e-13 2.679e-13
ENSMUSG00000077608 Gm22671 8.952 10.44 1.301e-13 3.144e-13
ENSMUSG00000103123 Gm37390 7.939 7.631 6.095e-14 1.575e-13
ENSMUSG00000103238 Gm36527 8.021 8.065 8.237e-14 2.076e-13
ENSMUSG00000102717 Gm37759 6.775 9.288 1.226e-13 2.982e-13
ENSMUSG00000093003 Mir3473c 8.471 8.36 1.002e-13 2.486e-13
ENSMUSG00000090533 Gm8214 7.655 7.567 6.579e-14 1.685e-13
ENSMUSG00000099648 Gm5975 7.963 7.704 7.376e-14 1.876e-13
ENSMUSG00000033007 Asic4 5.604 7.875 8.38e-14 2.107e-13
ENSMUSG00000008475 Arpc5 8.662 8.105 9.565e-14 2.384e-13
ENSMUSG00000041779 Tram2 6.528 7.662 7.747e-14 1.966e-13
ENSMUSG00000001138 Cnnm3 7.977 7.463 6.393e-14 1.645e-13
ENSMUSG00000059647 Cbx3-ps7 9.824 8.485 1.177e-13 2.882e-13
ENSMUSG00000050777 Tmem37 7.425 7.814 8.973e-14 2.251e-13
ENSMUSG00000048960 Prex2 9.349 8.314 1.207e-13 2.943e-13
ENSMUSG00000081562 Gm11575 7.415 7.499 7.833e-14 1.983e-13
ENSMUSG00000047539 Fbxo28 8.4 8.536 1.383e-13 3.313e-13
ENSMUSG00000088562 Gm23780 5.473 7.69 9.284e-14 2.324e-13
ENSMUSG00000099913 Gm28551 7.045 7.334 7.216e-14 1.839e-13
ENSMUSG00000095238 Gm4845 9.116 9.33 1.783e-13 4.234e-13
ENSMUSG00000102211 Gm37490 7.888 7.925 1.191e-13 2.91e-13
ENSMUSG00000025915 Sgk3 8.217 7.987 1.269e-13 3.074e-13
ENSMUSG00000082745 Gm15181 8.914 7.423 9.541e-14 2.383e-13
ENSMUSG00000099624 Gm7889 6.993 7.713 1.346e-13 3.245e-13
ENSMUSG00000104476 Gm38211 8.995 11.43 2.381e-13 5.526e-13
ENSMUSG00000084468 Gm22955 9.189 8.378 1.869e-13 4.422e-13
ENSMUSG00000043629 1700019D03Rik 8.757 7.684 1.349e-13 3.246e-13
ENSMUSG00000102687 Gm37219 7.401 7.316 1.024e-13 2.531e-13
ENSMUSG00000079045 Prox1os 7.071 7.4 1.141e-13 2.8e-13
ENSMUSG00000026199 Ankzf1 7.503 7.632 1.36e-13 3.266e-13
ENSMUSG00000026457 Adipor1 4.291 7.832 1.606e-13 3.823e-13
ENSMUSG00000089703 Gm15833 7.996 7.597 1.45e-13 3.466e-13
ENSMUSG00000060519 Tor3a 8.126 7.232 1.24e-13 3.01e-13
ENSMUSG00000102648 Gm38022 6.44 7.742 1.974e-13 4.629e-13
ENSMUSG00000016918 Sulf1 8.57 8.474 2.657e-13 6.128e-13
ENSMUSG00000087747 Gm25151 8.19 7.936 2.269e-13 5.278e-13
ENSMUSG00000085842 Pard3bos1 6.67 7.403 1.925e-13 4.524e-13
ENSMUSG00000080950 Gm7278 7.349 7.204 1.593e-13 3.8e-13
ENSMUSG00000049439 Cyp20a1 7.663 7.226 1.849e-13 4.382e-13
ENSMUSG00000065516 Mir214 10.28 8.018 3.071e-13 7.025e-13
ENSMUSG00000007805 Twist2 7.915 7.34 2.16e-13 5.044e-13
ENSMUSG00000026154 Sdhaf4 6.841 7.171 1.899e-13 4.482e-13
ENSMUSG00000039224 D1Pas1 7.063 7.586 2.694e-13 6.202e-13
ENSMUSG00000087949 Gm24251 8.314 8.097 3.502e-13 7.977e-13
ENSMUSG00000026200 Glb1l 6.497 7.161 2.138e-13 5.004e-13
ENSMUSG00000049528 Olfr429 4.318 7.888 3.764e-13 8.522e-13
ENSMUSG00000103751 Gm37283 5.32 6.932 1.911e-13 4.502e-13
ENSMUSG00000102961 Gm19918 6.635 7.222 2.474e-13 5.731e-13
ENSMUSG00000094651 Gal3st2 8.124 7.596 3.518e-13 7.998e-13
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ENSMUSG00000103072 Gm37905 8.011 6.971 6.493e-13 1.435e-12
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ENSMUSG00000095955 Gm22360 7.163 6.78 5.943e-13 1.324e-12
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ENSMUSG00000009905 Kdsr 7.608 6.983 2.788e-12 5.826e-12
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ENSMUSG00000041396 Mettl18 4.672 7.921 4.967e-12 1.015e-11
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ENSMUSG00000102433 Gm38050 5.422 8.681 1.032e-11 2.046e-11
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ENSMUSG00000083066 Gm12748 9.513 6.805 1.201e-11 2.359e-11
ENSMUSG00000100679 Gm28778 6.584 6.031 1.173e-11 2.31e-11
ENSMUSG00000100040 Gm28467 7.581 10.1 2.083e-11 4.056e-11
ENSMUSG00000100215 Gm8292 7.524 6.308 1.791e-11 3.506e-11
ENSMUSG00000053483 Usp21 6.07 6.583 2.032e-11 3.971e-11
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ENSMUSG00000098850 Gm27390 6.615 6.123 2.069e-11 4.037e-11
ENSMUSG00000026150 Mff 6.003 6.412 2.492e-11 4.828e-11
ENSMUSG00000026694 Mettl13 4.807 6.029 2.173e-11 4.224e-11
ENSMUSG00000090031 4732440D04Rik 6.445 5.997 2.376e-11 4.612e-11
ENSMUSG00000026536 Mnda 5.044 7.187 3.719e-11 7.144e-11
ENSMUSG00000026433 Rab29 5.021 6.417 3.354e-11 6.464e-11
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ENSMUSG00000099938 C030007H22Rik 7.535 7.244 4.365e-11 8.312e-11
ENSMUSG00000087131 Gm16152 8.314 6.99 4.235e-11 8.079e-11
ENSMUSG00000025925 Terf1 7.102 6.671 3.892e-11 7.449e-11
ENSMUSG00000090081 Gm16587 5.609 6.137 3.621e-11 6.966e-11
ENSMUSG00000103325 Gm10531 5.243 6.853 5.718e-11 1.081e-10
ENSMUSG00000101328 Rpl30-ps6 5.327 5.644 3.868e-11 7.416e-11
ENSMUSG00000026659 Dusp12 6.029 6.481 5.675e-11 1.075e-10
ENSMUSG00000103975 Gm37676 5.733 6.109 5.526e-11 1.049e-10
ENSMUSG00000102217 Gm37491 6.185 6.054 5.735e-11 1.083e-10
ENSMUSG00000104251 Gm18036 6.354 6.224 6.537e-11 1.23e-10
ENSMUSG00000099563 Gm19125 6.771 5.988 6.043e-11 1.139e-10
ENSMUSG00000088779 Gm26034 6.181 6.644 9.214e-11 1.725e-10
ENSMUSG00000046836 Brox 4.833 6.52 9.167e-11 1.719e-10
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ENSMUSG00000101826 Gm8581 6.118 5.645 9.529e-11 1.781e-10
ENSMUSG00000064954 Gm24919 6.094 5.986 1.055e-10 1.959e-10
ENSMUSG00000099949 Gm29124 8.009 6.816 1.334e-10 2.461e-10
ENSMUSG00000026675 Hsd17b7 8.649 7.765 1.451e-10 2.671e-10
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ENSMUSG00000100302 Gm29670 6.479 5.545 1.164e-10 2.154e-10
ENSMUSG00000102446 Gm17965 8.132 6.661 1.632e-10 2.99e-10
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ENSMUSG00000099868 Gm28792 6.196 5.716 1.63e-10 2.99e-10
ENSMUSG00000103517 Gm37432 6.385 5.559 1.941e-10 3.544e-10
ENSMUSG00000033364 Usp37 5.367 6.124 2.369e-10 4.313e-10
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ENSMUSG00000047048 Olfr432 6.191 6.913 2.699e-10 4.888e-10
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ENSMUSG00000039318 Rab3gap2 8.136 6.121 3.543e-10 6.387e-10
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ENSMUSG00000102709 Gm38308 9.458 6.113 4.037e-10 7.242e-10
ENSMUSG00000102459 Gm38352 6.005 6.347 4.464e-10 7.982e-10
ENSMUSG00000033701 Acbd6 6.359 5.248 3.614e-10 6.505e-10
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ENSMUSG00000102559 Gm37570 9.556 5.941 5.374e-10 9.593e-10
ENSMUSG00000100357 Gm28181 6.002 5.884 5.697e-10 1.015e-09
ENSMUSG00000025776 Crispld1 5.345 5.626 6.707e-10 1.192e-09
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ENSMUSG00000026394 Atp6v1g3 5.723 5.997 7.391e-10 1.309e-09
ENSMUSG00000040918 Slc19a2 3.623 6.175 8.395e-10 1.48e-09
ENSMUSG00000099562 Gm19027 4.715 5.312 8.04e-10 1.42e-09
ENSMUSG00000087374 Gm15457 6.006 5.269 7.97e-10 1.409e-09
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ENSMUSG00000026281 Dtymk 6.124 5.042 8.987e-10 1.579e-09
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ENSMUSG00000063558 Aox1 6.418 5.305 1.244e-09 2.168e-09
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ENSMUSG00000102714 Gm37618 5.799 4.807 1.378e-09 2.398e-09
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ENSMUSG00000025967 Eef1b2 5.546 4.902 1.727e-09 2.984e-09
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ENSMUSG00000067780 Pi15 4.542 5.292 1.918e-09 3.302e-09
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ENSMUSG00000100767 Gm3508 6.299 4.896 3.647e-09 6.147e-09
ENSMUSG00000102909 Gm2453 7.967 6.458 4.309e-09 7.229e-09
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ENSMUSG00000026447 Pik3c2b 7.763 4.851 4.596e-09 7.687e-09
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ENSMUSG00000026277 Stk25 5.16 5.087 5.856e-09 9.708e-09
ENSMUSG00000039342 Ankar 6.831 4.926 5.971e-09 9.883e-09
ENSMUSG00000096310 Gm23523 7.054 4.716 6.38e-09 1.054e-08
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ENSMUSG00000037499 Nenf 7.197 9.96 5.606e-09 9.322e-09
ENSMUSG00000060715 1700019A02Rik 6.41 5.406 7.99e-09 1.313e-08
ENSMUSG00000103380 Gm37756 6.594 4.671 7.846e-09 1.291e-08
ENSMUSG00000026156 B3gat2 6.417 4.616 7.833e-09 1.291e-08
ENSMUSG00000064936 Gm23722 4.923 4.602 8.172e-09 1.341e-08
ENSMUSG00000086726 Gm9458 3.824 4.504 8.539e-09 1.399e-08
ENSMUSG00000062580 Timm17a 8.847 5.446 9.406e-09 1.534e-08
ENSMUSG00000052477 C130026I21Rik 4.919 4.81 8.954e-09 1.465e-08
ENSMUSG00000102981 Gm38322 4.594 4.593 9.242e-09 1.51e-08
ENSMUSG00000100334 C230024C17Rik 5.198 5.864 1.14e-08 1.857e-08
ENSMUSG00000102718 Gm37761 4.485 4.904 1.163e-08 1.892e-08
ENSMUSG00000097760 6030442K20Rik 5.28 4.802 1.366e-08 2.219e-08
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ENSMUSG00000045381 Olfr433 3.065 4.982 1.743e-08 2.823e-08
ENSMUSG00000042581 Thsd7b 4.706 4.828 2.022e-08 3.264e-08
ENSMUSG00000102280 Gm36999 4.435 4.012 2.019e-08 3.264e-08
ENSMUSG00000055866 Per2 2.735 4.757 2.185e-08 3.522e-08
ENSMUSG00000025931 Paqr8 9.067 5.969 2.516e-08 4.045e-08
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ENSMUSG00000026701 Prdx6 4.681 4.356 2.661e-08 4.265e-08
ENSMUSG00000086056 Gm4846 4.452 3.916 2.669e-08 4.271e-08
ENSMUSG00000026131 Dst 8.552 5.156 2.906e-08 4.638e-08
ENSMUSG00000101249 Gm29216 3.963 4.047 2.872e-08 4.59e-08
ENSMUSG00000077274 Gm22786 3.48 4.081 2.95e-08 4.701e-08
ENSMUSG00000026639 Lamb3 4.687 5.48 3.219e-08 5.109e-08
ENSMUSG00000083118 Gm11609 4.722 6.231 3.206e-08 5.095e-08
ENSMUSG00000102447 Gm6621 8.134 4.504 3.128e-08 4.978e-08
ENSMUSG00000064967 Gm24405 4.491 4.642 3.263e-08 5.171e-08
ENSMUSG00000093064 Gm23153 7.862 6.025 3.622e-08 5.731e-08
ENSMUSG00000101846 Gm19927 6.734 5.574 3.672e-08 5.803e-08
ENSMUSG00000101450 Gm28941 5.375 5.851 4.214e-08 6.65e-08
ENSMUSG00000026024 Als2 4.008 4.036 4.684e-08 7.381e-08
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ENSMUSG00000100580 4933436I20Rik 9.246 5.651 5.038e-08 7.916e-08
ENSMUSG00000102401 Gm37864 5.906 4.649 5.121e-08 8.035e-08
ENSMUSG00000102245 Gm37713 6.131 4.796 5.175e-08 8.108e-08
ENSMUSG00000104316 Gm37909 5.064 4.684 5.303e-08 8.285e-08
ENSMUSG00000018199 Trove2 3.661 4.209 5.268e-08 8.242e-08
ENSMUSG00000101223 4930568A12Rik 4.759 5.336 5.517e-08 8.596e-08
ENSMUSG00000064389 Gm26446 5.109 3.916 5.339e-08 8.33e-08
ENSMUSG00000026209 Dnpep 8.457 5.152 5.673e-08 8.826e-08
ENSMUSG00000102650 Gm19863 7.237 5.062 5.707e-08 8.867e-08
ENSMUSG00000082935 Gm7658 4.307 4.937 5.932e-08 9.192e-08
ENSMUSG00000026637 Traf5 4.966 3.811 5.733e-08 8.895e-08
ENSMUSG00000026141 Col19a1 5.371 5.099 6.259e-08 9.657e-08
ENSMUSG00000026189 Pecr 5.94 4.857 6.252e-08 9.657e-08
ENSMUSG00000042751 Nmnat2 4.03 4.283 6.18e-08 9.562e-08
ENSMUSG00000094820 Gm23805 4.664 4.797 6.305e-08 9.715e-08
ENSMUSG00000089888 Gm16547 4.487 4.482 7.169e-08 1.103e-07
ENSMUSG00000102508 Gm37367 6.526 4.152 9.599e-08 1.475e-07
ENSMUSG00000051251 Nhlh1 4.677 4.609 1.011e-07 1.552e-07
ENSMUSG00000030768 Disp1 7.378 4.253 1.055e-07 1.616e-07
ENSMUSG00000010453 Kansl3 3.773 4.484 1.079e-07 1.651e-07
ENSMUSG00000067604 Nms 6.834 3.625 1.297e-07 1.983e-07
ENSMUSG00000053963 Stum 5.576 4.674 1.358e-07 2.072e-07
ENSMUSG00000097711 Gm5523 2.716 4.152 1.4e-07 2.134e-07
ENSMUSG00000099843 Gm7160 5.795 5.449 1.424e-07 2.168e-07
ENSMUSG00000025964 Adam23 6.823 4.937 1.461e-07 2.221e-07
ENSMUSG00000102235 Gm37886 8.065 4.623 1.463e-07 2.221e-07
ENSMUSG00000039997 Ifi203 3.217 4.645 1.529e-07 2.317e-07
ENSMUSG00000026458 Ppfia4 5.867 3.878 1.641e-07 2.484e-07
ENSMUSG00000103928 Gm37893 3.419 3.898 1.681e-07 2.541e-07
ENSMUSG00000096992 Gm26788 4.133 4.726 1.748e-07 2.635e-07
ENSMUSG00000101921 Gm28825 5.297 4.51 1.748e-07 2.635e-07
ENSMUSG00000102139 Gm37109 3.442 4.696 1.794e-07 2.701e-07
ENSMUSG00000026036 Nif3l1 5.224 3.9 1.797e-07 2.701e-07
ENSMUSG00000026471 Mr1 7.52 4.175 1.873e-07 2.812e-07
ENSMUSG00000059089 Fcgr4 3.021 4.328 2.035e-07 3.051e-07
ENSMUSG00000006403 Adamts4 3.702 3.74 2.067e-07 3.096e-07
ENSMUSG00000026072 Il1r1 2.959 4.383 2.132e-07 3.188e-07
ENSMUSG00000077939 Gm24133 4.254 4.205 2.365e-07 3.532e-07
ENSMUSG00000045968 Teddm2 6.011 4.564 2.476e-07 3.692e-07
ENSMUSG00000005674 Tomm40l 4.427 4.292 2.505e-07 3.726e-07
ENSMUSG00000026663 Atf6 5.711 4.544 2.517e-07 3.738e-07
ENSMUSG00000100595 Gm19087 7.124 5.345 2.492e-07 3.712e-07
ENSMUSG00000098833 Mir6349 4.551 3.856 2.642e-07 3.919e-07
ENSMUSG00000103780 Gm37524 3.078 4.648 2.663e-07 3.944e-07
ENSMUSG00000090071 Cdk5r2 4.523 4.334 2.713e-07 4.014e-07
ENSMUSG00000079671 2610203C22Rik 3.221 4.425 2.779e-07 4.1e-07
ENSMUSG00000026641 Usf1 3.801 3.693 2.761e-07 4.08e-07
ENSMUSG00000093879 Gm25457 4.885 4.626 2.811e-07 4.142e-07
ENSMUSG00000086706 Gm15848 5.382 4.328 2.86e-07 4.204e-07
ENSMUSG00000064860 n-R5s220 3.235 3.815 2.861e-07 4.204e-07
ENSMUSG00000102413 Gm36937 2.997 4.508 2.89e-07 4.237e-07
ENSMUSG00000103307 Gm19335 6.979 4.632 2.891e-07 4.237e-07
ENSMUSG00000098351 Gm27507 5.658 3.933 3.212e-07 4.702e-07
ENSMUSG00000079554 Aox2 5.103 5.523 3.254e-07 4.757e-07
ENSMUSG00000081261 Gm15370 2.987 3.581 3.392e-07 4.952e-07
ENSMUSG00000101567 Txn-ps1 3.095 5.518 3.425e-07 4.994e-07
ENSMUSG00000026021 Sumo1 3.753 5.007 3.618e-07 5.269e-07
ENSMUSG00000049515 Espnl 3.51 3.657 3.692e-07 5.369e-07
ENSMUSG00000099694 Gm29610 4.97 4.597 3.795e-07 5.512e-07
ENSMUSG00000016262 Sertad4 4.467 4.195 3.924e-07 5.691e-07
ENSMUSG00000056211 R3hdm1 3.433 3.394 3.998e-07 5.792e-07
ENSMUSG00000067017 Gm3608 7.677 4.794 4.102e-07 5.934e-07
ENSMUSG00000037995 Igsf9 9.078 5.256 4.126e-07 5.961e-07
ENSMUSG00000101600 4930599A14Rik 4.175 3.405 4.712e-07 6.791e-07
ENSMUSG00000102194 Gm38169 2.598 4.449 4.839e-07 6.964e-07
ENSMUSG00000100455 Gm29170 7.738 6.789 4.574e-07 6.601e-07
ENSMUSG00000026414 Tnnt2 3.093 3.405 5.734e-07 8.243e-07
ENSMUSG00000026390 Marco 3.927 3.913 5.873e-07 8.431e-07
ENSMUSG00000103101 Gm37048 2.501 3.896 6.002e-07 8.606e-07
ENSMUSG00000084558 Gm24836 3.942 4.263 6.061e-07 8.679e-07
ENSMUSG00000103076 Gm37902 2.243 3.773 6.118e-07 8.748e-07
ENSMUSG00000103288 Gm37273 2.337 3.983 6.485e-07 9.262e-07
ENSMUSG00000036086 Zranb3 2.778 4.065 6.542e-07 9.332e-07
ENSMUSG00000087859 Gm22796 4.454 3.617 7.378e-07 1.051e-06
ENSMUSG00000088291 Gm26412 3.475 3.773 7.438e-07 1.056e-06
ENSMUSG00000099945 1700126A01Rik 4.302 4.405 7.427e-07 1.055e-06
ENSMUSG00000089831 Gm15508 2.161 3.623 7.641e-07 1.083e-06
ENSMUSG00000103736 Gm37650 5.01 4.756 7.724e-07 1.093e-06
ENSMUSG00000104069 Gm37198 3.214 3.605 7.882e-07 1.113e-06
ENSMUSG00000100761 Gm29344 6.178 6.129 7.385e-07 1.051e-06
ENSMUSG00000098306 Gm28040 3.327 3.579 8.557e-07 1.207e-06
ENSMUSG00000026305 Lrrfip1 6.004 5.842 7.835e-07 1.108e-06
ENSMUSG00000042046 Dstyk 6.805 4.451 8.9e-07 1.253e-06
ENSMUSG00000103016 Gm37506 3.839 3.618 9.12e-07 1.283e-06
ENSMUSG00000099922 Gm29016 5.259 3.405 9.196e-07 1.292e-06
ENSMUSG00000026295 Spp2 5.232 4.072 1.019e-06 1.43e-06
ENSMUSG00000026630 Batf3 2.71 3.996 1.025e-06 1.437e-06
ENSMUSG00000093823 Gm24802 3.862 3.215 1.068e-06 1.493e-06
ENSMUSG00000026526 Fh1 6.8 3.464 1.07e-06 1.494e-06
ENSMUSG00000100941 Gm29597 5.249 5.433 1.03e-06 1.441e-06
ENSMUSG00000102897 Gm37853 3.396 4.164 1.152e-06 1.606e-06
ENSMUSG00000102275 Gm37144 3.316 3.576 1.536e-06 2.139e-06
ENSMUSG00000025993 Slc40a1 5.763 4.886 1.565e-06 2.177e-06
ENSMUSG00000091020 Gm5828 4.032 4.814 1.637e-06 2.274e-06
ENSMUSG00000026434 Nucks1 5.471 4.337 1.82e-06 2.525e-06
ENSMUSG00000102612 Gm37845 7.776 4.77 1.855e-06 2.57e-06
ENSMUSG00000025912 Mybl1 5.587 3.686 1.979e-06 2.738e-06
ENSMUSG00000087561 Gm11608 3.019 4.454 2.076e-06 2.87e-06
ENSMUSG00000103382 Gm37755 4.013 3.088 2.298e-06 3.173e-06
ENSMUSG00000077358 Gm26293 3.418 3.084 2.345e-06 3.233e-06
ENSMUSG00000104414 Gm37799 3.098 3.277 2.402e-06 3.307e-06
ENSMUSG00000104424 Gm19058 2.955 3.204 2.503e-06 3.438e-06
ENSMUSG00000099906 Gm28653 4.21 4.704 2.411e-06 3.316e-06
ENSMUSG00000026023 Cdk15 5.022 2.91 2.578e-06 3.532e-06
ENSMUSG00000102299 Gm37539 2.883 4.641 2.57e-06 3.526e-06
ENSMUSG00000052688 Rab7b 3.987 3.201 2.888e-06 3.952e-06
ENSMUSG00000102676 Gm37435 2.055 3.377 3.028e-06 4.139e-06
ENSMUSG00000026280 Atg4b 2.976 2.907 3.155e-06 4.297e-06
ENSMUSG00000083582 Gm15393 3.577 3.277 3.154e-06 4.297e-06
ENSMUSG00000104435 Gm37422 3.143 2.894 3.206e-06 4.351e-06
ENSMUSG00000102133 Gm37106 2.161 3.215 3.202e-06 4.351e-06
ENSMUSG00000037461 Ints7 2.908 3.319 3.205e-06 4.351e-06
ENSMUSG00000103270 Gm37915 2.603 4.486 3.126e-06 4.268e-06
ENSMUSG00000103958 Gm8023 5.24 3.583 3.257e-06 4.414e-06
ENSMUSG00000099377 Gm6159 2.357 3.957 3.403e-06 4.606e-06
ENSMUSG00000097628 Gm2383 2.253 3.386 3.493e-06 4.722e-06
ENSMUSG00000033159 Cnppd1 2.801 3.323 3.524e-06 4.758e-06
ENSMUSG00000026198 Abcb6 1.963 3.209 3.917e-06 5.277e-06
ENSMUSG00000058729 Lin9 6.862 5.163 3.606e-06 4.863e-06
ENSMUSG00000100221 4930532M18Rik 2.114 3.505 4.044e-06 5.441e-06
ENSMUSG00000089670 Gm16581 3.143 3.493 4.256e-06 5.72e-06
ENSMUSG00000100267 Gm28779 3.454 3.55 4.275e-06 5.738e-06
ENSMUSG00000099878 Gm28178 1.963 3.204 4.43e-06 5.938e-06
ENSMUSG00000100977 Gm29334 2.024 3.326 4.556e-06 6.1e-06
ENSMUSG00000067028 Cntnap5b 2.349 3.589 5.207e-06 6.964e-06
ENSMUSG00000026288 Inpp5d 6.491 4.194 5.324e-06 7.111e-06
ENSMUSG00000026439 Rbbp5 2.345 3.377 5.574e-06 7.437e-06
ENSMUSG00000079139 Gm4204 2.802 3.188 6.183e-06 8.24e-06
ENSMUSG00000102839 Gm37385 2.604 3.405 6.275e-06 8.352e-06
ENSMUSG00000041809 Efhc1 3.876 3.617 6.322e-06 8.405e-06
ENSMUSG00000091199 Gm2619 3.273 3.277 6.577e-06 8.733e-06
ENSMUSG00000103984 Gm37447 2.285 3.261 6.589e-06 8.738e-06
ENSMUSG00000101890 Gm3116 2.299 3.302 7.94e-06 1.049e-05
ENSMUSG00000100711 Gm29461 2.731 3.773 7.834e-06 1.037e-05
ENSMUSG00000104111 Gm37294 8.667 5.977 6.743e-06 8.932e-06
ENSMUSG00000085311 Gm15445 3.88 3.464 8.415e-06 1.111e-05
ENSMUSG00000026638 Irf6 6.002 3.261 1.033e-05 1.36e-05
ENSMUSG00000097970 Gm27028 2.884 3.521 1.029e-05 1.356e-05
ENSMUSG00000102090 Gm8964 3.197 4.272 1.166e-05 1.533e-05
ENSMUSG00000102618 Gm37848 7.39 3.692 1.36e-05 1.783e-05
ENSMUSG00000103548 Gm37670 3.563 3.084 1.396e-05 1.825e-05
ENSMUSG00000004707 Ly9 4.045 4.089 1.347e-05 1.768e-05
ENSMUSG00000106377 Gm44300 8.626 4.688 1.338e-05 1.758e-05
ENSMUSG00000073664 Nbeal1 5.735 3.187 1.451e-05 1.895e-05
ENSMUSG00000089790 Gm16588 2.483 3.084 1.502e-05 1.96e-05
ENSMUSG00000098407 Mir6348 4.233 3.36 1.525e-05 1.987e-05
ENSMUSG00000101168 Gm28892 9.329 5.264 1.386e-05 1.815e-05
ENSMUSG00000041670 Rims1 7.78 3.883 1.548e-05 2.015e-05
ENSMUSG00000104410 Gm37066 7.336 3.899 1.619e-05 2.105e-05
ENSMUSG00000100872 1700065J18Rik 6.888 3.607 1.699e-05 2.206e-05
ENSMUSG00000038235 F11r 5.423 3.098 1.776e-05 2.303e-05
ENSMUSG00000039748 Exo1 3.265 3.2 1.803e-05 2.332e-05
ENSMUSG00000097988 Gm10535 2.451 3.405 1.79e-05 2.319e-05
ENSMUSG00000102408 4930568G15Rik 4.924 3.809 1.825e-05 2.358e-05
ENSMUSG00000100777 4930598F16Rik 3.377 3.118 1.999e-05 2.581e-05
ENSMUSG00000070870 Cryge 2.193 3.084 2.199e-05 2.831e-05
ENSMUSG00000077328 Gm25832 3.289 3.884 2.131e-05 2.748e-05
ENSMUSG00000100053 Gm28154 3.046 3.032 2.428e-05 3.12e-05
ENSMUSG00000006007 Pdc 4.365 3.916 2.405e-05 3.093e-05
ENSMUSG00000026525 Opn3 6.452 3.499 2.633e-05 3.379e-05
ENSMUSG00000103516 Gm37431 3.083 3.141 2.765e-05 3.541e-05
ENSMUSG00000026271 Gpr35 4.837 3.858 2.699e-05 3.46e-05
ENSMUSG00000060679 Mrps9 6.77 3.525 2.857e-05 3.649e-05
ENSMUSG00000026430 Rassf5 2.359 2.895 3.065e-05 3.911e-05
ENSMUSG00000026219 Trip12 8.811 4.927 2.775e-05 3.549e-05
ENSMUSG00000103137 Gm37981 2.931 3.012 3.265e-05 4.162e-05
ENSMUSG00000073478 D730003I15Rik 2.959 2.91 3.293e-05 4.192e-05
ENSMUSG00000094497 Gm8210 3.071 2.529 3.571e-05 4.531e-05
ENSMUSG00000087721 Gm26280 5.3 3.652 3.415e-05 4.342e-05
ENSMUSG00000026667 Uhmk1 2.345 3.084 3.534e-05 4.489e-05
ENSMUSG00000089648 Gm15790 4.211 2.706 3.863e-05 4.884e-05
ENSMUSG00000033569 Adgrb3 7.003 3.586 3.712e-05 4.704e-05
ENSMUSG00000086064 4930439A04Rik 2.406 3.228 3.791e-05 4.799e-05
ENSMUSG00000047021 Cfap65 6.864 2.971 3.925e-05 4.952e-05
ENSMUSG00000026628 Atf3 2.864 3.588 3.882e-05 4.904e-05
ENSMUSG00000101955 Gm29376 2.161 2.907 4.111e-05 5.175e-05
ENSMUSG00000026620 Mark1 2.514 3.952 3.977e-05 5.012e-05
ENSMUSG00000102193 Gm7299 3.148 2.703 4.254e-05 5.349e-05
ENSMUSG00000026509 Capn2 5.609 2.971 5.234e-05 6.574e-05
ENSMUSG00000082373 Gm13754 2.71 2.336 5.551e-05 6.956e-05
ENSMUSG00000089057 Gm23349 2.915 3.115 5.527e-05 6.933e-05
ENSMUSG00000100253 1700020N18Rik 2.933 3.78 5.711e-05 7.148e-05
ENSMUSG00000103768 Gm37856 2.008 2.937 6.137e-05 7.664e-05
ENSMUSG00000062510 Nsl1 3.455 3.666 6.09e-05 7.614e-05
ENSMUSG00000026572 Tbx19 3.401 2.656 6.474e-05 8.068e-05
ENSMUSG00000026580 Selp 3.473 2.827 6.54e-05 8.12e-05
ENSMUSG00000032719 Sbspon 2.299 3.274 6.416e-05 8.004e-05
ENSMUSG00000102372 A430050A11Rik 3.421 3.011 6.521e-05 8.108e-05
ENSMUSG00000073490 Ifi207 2.61 3.392 6.518e-05 8.108e-05
ENSMUSG00000026626 Ppp2r5a 4.298 3.182 6.752e-05 8.366e-05
ENSMUSG00000104007 Gm38332 3.307 3.992 6.546e-05 8.12e-05
ENSMUSG00000085653 Gm15179 6.411 3.071 6.997e-05 8.651e-05
ENSMUSG00000103824 Gm38177 5.368 2.656 7.174e-05 8.86e-05
ENSMUSG00000065533 Mir205 5.747 2.944 7.282e-05 8.974e-05
ENSMUSG00000002459 Rgs20 4.755 2.336 7.57e-05 9.307e-05
ENSMUSG00000076999 Gm22426 4.616 3.978 6.969e-05 8.626e-05
ENSMUSG00000101429 BC055402 2.512 2.529 7.642e-05 9.387e-05
ENSMUSG00000104077 Gm37027 3.125 4.017 7.218e-05 8.904e-05
ENSMUSG00000041605 5730559C18Rik 2.069 2.656 7.976e-05 9.775e-05
ENSMUSG00000103706 6820402A03Rik 3.59 3.121 7.812e-05 9.584e-05
ENSMUSG00000100480 Gm29156 5.222 4.247 7.488e-05 9.218e-05
ENSMUSG00000026248 Mrpl44 3.201 2.529 8.756e-05 0.0001071
ENSMUSG00000025930 Msc 3.689 3.831 8.177e-05 0.0001001
ENSMUSG00000026173 Plcd4 3.129 3.013 9.485e-05 0.0001159
ENSMUSG00000087538 Gm16341 2.716 3.037 9.51e-05 0.000116
ENSMUSG00000043263 Ifi209 3.495 2.996 0.0001027 0.0001251
ENSMUSG00000101721 Gm8354 1.995 2.719 0.0001086 0.0001323
ENSMUSG00000026123 Plekhb2 4.174 2.665 0.0001103 0.0001341
ENSMUSG00000103482 Gm37999 4.382 2.529 0.0001138 0.0001379
ENSMUSG00000025779 Ly96 3.46 2.846 0.0001116 0.0001356
ENSMUSG00000104072 4930448I06Rik 3.579 2.703 0.0001135 0.0001378
ENSMUSG00000109599 Gm31812 1.81 2.525 0.0001147 0.0001389
ENSMUSG00000101483 1700016L21Rik 3.093 2.846 0.000122 0.0001474
ENSMUSG00000089420 Gm27934 3.318 2.707 0.0001234 0.0001489
ENSMUSG00000085184 4933439K11Rik 3.497 3.138 0.0001211 0.0001465
ENSMUSG00000056699 Gm5533 4.141 2.923 0.0001342 0.0001616
ENSMUSG00000065435 Mir135b 2.704 3.071 0.0001362 0.0001638
ENSMUSG00000013275 Slc41a1 4.33 4.081 0.0001267 0.0001528
ENSMUSG00000064281 Rpl19-ps1 4.531 2.63 0.0001451 0.0001742
ENSMUSG00000026064 Ptp4a1 3.662 3.045 0.000142 0.0001706
ENSMUSG00000038242 Aox4 3.056 2.823 0.0001495 0.0001792
ENSMUSG00000102608 Gm37267 2.253 2.656 0.0001594 0.0001907
ENSMUSG00000088164 Gm23534 2.625 3.169 0.0001577 0.0001888
ENSMUSG00000047216 Cdh19 6.097 2.95 0.0001664 0.0001989
ENSMUSG00000101378 Gm28086 3.324 2.963 0.0001744 0.0002083
ENSMUSG00000103357 Gm7804 7.026 3.297 0.0001757 0.0002094
ENSMUSG00000101798 Gm28995 7.097 3.334 0.0001755 0.0002093
ENSMUSG00000097845 A230020J21Rik 4.964 4.023 0.0001876 0.0002233
ENSMUSG00000089838 Gm2962 3.747 3.1 0.0002167 0.0002573
ENSMUSG00000088953 Gm24942 2.99 2.897 0.0002278 0.0002702
ENSMUSG00000025934 Gsta3 4.767 4.577 0.0002045 0.0002431
ENSMUSG00000101523 Gm10031 3.037 3.173 0.0002312 0.000274
ENSMUSG00000103999 Gm38026 2.299 2.703 0.0002468 0.0002918
ENSMUSG00000102982 Gm38319 2.101 2.529 0.0002563 0.0003025
ENSMUSG00000104423 A030005K14Rik 4.315 2.336 0.0002614 0.0003079
ENSMUSG00000097608 Gm7952 3.197 2.336 0.0002656 0.0003125
ENSMUSG00000101939 Gm28438 2.964 3.039 0.0002588 0.0003051
ENSMUSG00000026029 Casp8 3.729 3.521 0.0002556 0.0003019
ENSMUSG00000099592 Gm5264 2.299 2.725 0.0002696 0.0003169
ENSMUSG00000100831 Gm17847 1.718 2.336 0.0002805 0.0003289
ENSMUSG00000026475 Rgs16 1.718 2.336 0.0002805 0.0003289
ENSMUSG00000099568 Gm28513 9.099 4.81 0.0002339 0.0002769
ENSMUSG00000102254 Gm7496 2.193 2.657 0.0002857 0.0003343
ENSMUSG00000090246 Gm17017 3.012 3.493 0.000284 0.0003327
ENSMUSG00000101154 Gm17777 5.696 2.529 0.0003065 0.0003579
ENSMUSG00000025900 Rp1 2.055 2.786 0.0003018 0.0003527
ENSMUSG00000088813 Gm22383 2.962 3.1 0.0003185 0.0003715
ENSMUSG00000047180 Neurl3 2.285 2.128 0.0003789 0.0004415
ENSMUSG00000103089 Gm38118 2.684 3.137 0.0004039 0.0004697
ENSMUSG00000051985 Igfn1 3.287 2.86 0.000418 0.0004856
ENSMUSG00000026147 Col9a1 3.351 3.704 0.0004034 0.0004696
ENSMUSG00000090222 Ifi203-ps 2.116 2.651 0.0004403 0.0005109
ENSMUSG00000026565 Pou2f1 1.963 2.461 0.0004514 0.0005227
ENSMUSG00000026035 Ppil3 1.718 2.311 0.0004712 0.0005451
ENSMUSG00000026125 Prss39 2.736 3.037 0.0004506 0.0005223
ENSMUSG00000026567 Adcy10 2.101 2.121 0.0005139 0.0005938
ENSMUSG00000103280 Gm37277 2.992 1.933 0.0005605 0.000647
ENSMUSG00000045515 Pou3f3 4.26 1.933 0.0005925 0.0006826
ENSMUSG00000064845 Gm26203 2.273 2.507 0.0005851 0.0006748
ENSMUSG00000100635 Gm29157 2.467 1.931 0.0006729 0.0007743
ENSMUSG00000041926 Rnpep 2.207 1.933 0.0006793 0.0007801
ENSMUSG00000103443 Gm37132 2.253 2.121 0.0006789 0.0007801
ENSMUSG00000097893 1700034P13Rik 2.193 2.112 0.0006873 0.0007885
ENSMUSG00000104164 Gm38248 4.188 2.311 0.0007006 0.0008029
ENSMUSG00000103510 Gm30725 2.451 2.514 0.0007265 0.0008317
ENSMUSG00000100561 Gm7516 5.214 1.931 0.000798 0.0009099
ENSMUSG00000103492 Gm37412 2.253 1.931 0.000812 0.0009233
ENSMUSG00000102499 Gm36984 5.608 1.931 0.0008123 0.0009233
ENSMUSG00000101126 Gm10538 2.559 2.665 0.000798 0.0009099
ENSMUSG00000104121 Gm37485 4.584 2.638 0.0008049 0.0009168
ENSMUSG00000079434 Neu2 4.258 3.209 0.0007807 0.0008929
ENSMUSG00000100850 4930434B07Rik 2.914 3.239 0.0007833 0.000895
ENSMUSG00000103560 Gm38070 2.467 2.121 0.0008592 0.0009757
ENSMUSG00000041559 Fmod 2.889 2.289 0.0009363 0.001062
ENSMUSG00000099835 Gm29668 2.314 1.931 0.0009501 0.001076
ENSMUSG00000097797 Gm26901 2.451 2.631 0.000941 0.001066
ENSMUSG00000100269 Gm28782 4.157 1.933 0.0009746 0.001102
ENSMUSG00000026670 Uap1 4.445 2.121 0.0009852 0.001113
ENSMUSG00000060244 Alyref2 2.962 2.112 0.000996 0.001124
ENSMUSG00000104248 Gm38152 1.81 1.933 0.001005 0.001133
ENSMUSG00000049598 Vsig8 3.625 2.107 0.001013 0.00114
ENSMUSG00000100701 Gm19587 2.253 1.931 0.001067 0.001199
ENSMUSG00000102266 Gm38106 6.235 3.48 0.00101 0.001138
ENSMUSG00000070645 Ren1 4.255 1.931 0.001144 0.001283
ENSMUSG00000036104 Rab3gap1 1.718 1.933 0.001203 0.001345
ENSMUSG00000051590 Map3k19 6.129 2.536 0.001179 0.001321
ENSMUSG00000054493 Gm9947 2.58 2.289 0.001197 0.00134
ENSMUSG00000026175 Vil1 3.404 2.097 0.001222 0.001366
ENSMUSG00000104416 Gm37067 1.718 1.933 0.00126 0.001407
ENSMUSG00000103756 Gm37285 2.71 1.931 0.001277 0.001423
ENSMUSG00000100503 Gm28927 2.735 1.933 0.001279 0.001424
ENSMUSG00000077759 Gm23528 2.359 1.931 0.001284 0.001428
ENSMUSG00000103374 Gm37239 2.137 2.581 0.001269 0.001415
ENSMUSG00000026700 Tnfsf4 5.333 2.107 0.001342 0.00149
ENSMUSG00000102956 Gm37017 2.314 1.933 0.001382 0.001532
ENSMUSG00000089866 Gm15699 5.432 2.329 0.001376 0.001527
ENSMUSG00000102134 Gm9931 5.209 2.22 0.001422 0.001575
ENSMUSG00000102414 Gm36938 4.403 5.443 0.001093 0.001227
ENSMUSG00000082059 Gm15183 5.383 2.114 0.001602 0.001772
ENSMUSG00000101945 Gm29138 3.046 1.933 0.001692 0.001868
ENSMUSG00000104157 Gm38101 3.574 2.114 0.001683 0.00186
ENSMUSG00000038218 Gm9765 2.299 1.933 0.001705 0.00188
ENSMUSG00000102845 Gm38033 1.81 1.536 0.001722 0.001896
ENSMUSG00000104366 4933409D19Rik 2.299 1.933 0.001732 0.001905
ENSMUSG00000103272 Gm37914 4.257 2.569 0.001708 0.001882
ENSMUSG00000104379 Gm37509 2.527 2.201 0.001772 0.001947
ENSMUSG00000104291 A130071D04Rik 3.35 2.112 0.001941 0.00213
ENSMUSG00000101337 Dnah7c 4.616 1.933 0.001961 0.00215
ENSMUSG00000070489 4930527J03Rik 1.963 1.933 0.002016 0.002206
ENSMUSG00000025927 Tfap2b 6.074 2.415 0.001987 0.002177
ENSMUSG00000090243 Gm16103 1.902 1.933 0.002066 0.002259
ENSMUSG00000026544 Dusp23 2.823 1.72 0.002121 0.002316
ENSMUSG00000041782 Lad1 3.429 1.536 0.002209 0.00241
ENSMUSG00000097615 Gm2061 2.705 2.112 0.002232 0.002433
ENSMUSG00000103953 Gm29718 3.059 2.201 0.00225 0.00245
ENSMUSG00000104097 Gm37313 2.055 1.536 0.002409 0.002618
ENSMUSG00000041889 Shisa4 1.902 2.097 0.002447 0.002654
ENSMUSG00000006546 Cryba2 2.834 2.289 0.002446 0.002654
ENSMUSG00000104397 Gm37958 2.477 1.536 0.002479 0.002683
ENSMUSG00000094768 Gm25524 2.055 2.289 0.002463 0.002669
ENSMUSG00000095942 Gm25198 2.943 1.536 0.002497 0.002701
ENSMUSG00000099208 Gm27940 2.208 1.536 0.002513 0.002715
ENSMUSG00000102915 Gm32460 5.557 3.462 0.002279 0.00248
ENSMUSG00000102427 Gm37463 3.093 1.536 0.002614 0.002821
ENSMUSG00000103591 Gm38365 2.055 2.128 0.002668 0.002877
ENSMUSG00000065468 Mir26b 2.008 1.933 0.002743 0.002955
ENSMUSG00000104010 Gm37366 2.63 1.536 0.002794 0.003005
ENSMUSG00000033740 St18 6.468 2.31 0.00278 0.002992
ENSMUSG00000073650 Catip 1.963 1.933 0.002979 0.0032
ENSMUSG00000067879 3110035E14Rik 5.115 2.131 0.003043 0.003266
ENSMUSG00000093155 Gm25035 2.42 1.536 0.003132 0.003358
ENSMUSG00000099784 Dalir 2.649 1.536 0.003186 0.003409
ENSMUSG00000102491 Gm36986 2.405 2.311 0.003235 0.003459
ENSMUSG00000102521 Gm37781 2.055 1.536 0.003333 0.003557
ENSMUSG00000103679 Gm37623 2.959 2.738 0.003184 0.003409
ENSMUSG00000070594 Gm4788 2.586 2.336 0.003346 0.003567
ENSMUSG00000104165 Gm38249 1.995 1.931 0.003402 0.003623
ENSMUSG00000084228 Gm8080 2.161 2.78 0.003267 0.003489
ENSMUSG00000101942 Gm19582 2.055 2.121 0.003581 0.00381
ENSMUSG00000043429 Ccdc185 3.413 1.536 0.003671 0.003902
ENSMUSG00000040265 Dnm3 1.995 1.536 0.003716 0.003946
ENSMUSG00000016181 Diexf 1.963 1.536 0.00372 0.003947
ENSMUSG00000104433 Gm37426 2.923 1.72 0.003816 0.004044
ENSMUSG00000042111 Ccdc115 2.766 1.933 0.003822 0.004047
ENSMUSG00000025959 Klf7 3.353 1.72 0.00395 0.004178
ENSMUSG00000077413 Gm23389 4.311 1.536 0.004026 0.004255
ENSMUSG00000097014 Gm26706 3.844 1.536 0.004201 0.004435
ENSMUSG00000026436 Elk4 1.963 1.536 0.004211 0.004438
ENSMUSG00000026596 Abl2 1.963 1.536 0.004211 0.004438
ENSMUSG00000082769 Gm15793 1.963 1.931 0.004219 0.004442
ENSMUSG00000039354 Smarcal1 4.46 1.536 0.004236 0.004456
ENSMUSG00000102374 Gm38387 4.431 1.536 0.004317 0.004537
ENSMUSG00000078193 Gm2000 3.371 1.536 0.004467 0.004686
ENSMUSG00000091937 Gm3646 5.695 2.311 0.004436 0.004657
ENSMUSG00000103963 Gm30932 2.989 1.536 0.004766 0.004995
ENSMUSG00000034220 Gpc1 2.008 1.933 0.004774 0.004998
ENSMUSG00000103085 Gm38120 1.963 1.536 0.004783 0.005003
ENSMUSG00000101702 1700072G22Rik 2.161 1.536 0.004808 0.005024
ENSMUSG00000097416 Gm26670 2.055 1.536 0.004817 0.005029
ENSMUSG00000102331 Gm19938 3.324 1.536 0.004893 0.005104
ENSMUSG00000089291 Gm23208 2.101 1.536 0.004924 0.005131
ENSMUSG00000101617 Gm6947 2.859 1.536 0.004983 0.005188
ENSMUSG00000099227 Mir8114 2.243 1.935 0.005015 0.005216
ENSMUSG00000104253 Gm37174 2.76 1.536 0.005055 0.005253
ENSMUSG00000044757 Gm6430 2.752 1.72 0.00519 0.005388
ENSMUSG00000052759 Gpr25 3.181 1.931 0.005195 0.005388
ENSMUSG00000104474 Gm38210 2.197 1.536 0.005243 0.005434
ENSMUSG00000113925 Gm7544 2.243 1.536 0.005287 0.005474
ENSMUSG00000032883 Acsl3 3.98 1.72 0.005328 0.005511
ENSMUSG00000103114 Gm32200 2.045 1.536 0.005367 0.005546
ENSMUSG00000097251 5033417F24Rik 2.229 1.536 0.005398 0.005574
ENSMUSG00000026502 Desi2 2.99 1.536 0.005449 0.00562
ENSMUSG00000085391 Gm16150 2.906 2.201 0.005503 0.005671
ENSMUSG00000050296 Abca12 4.106 1.536 0.005707 0.005876
ENSMUSG00000102386 2900022M07Rik 3.029 1.72 0.00573 0.005894
ENSMUSG00000109887 Gm28756 3.033 1.536 0.005969 0.006134
ENSMUSG00000102546 Gm9722 2.533 1.536 0.006008 0.006164
ENSMUSG00000104136 Gm36955 2.024 1.931 0.006009 0.006164
ENSMUSG00000103139 Gm37982 4.274 1.536 0.006214 0.006369
ENSMUSG00000064395 Gm23623 2.799 1.842 0.006493 0.006648
ENSMUSG00000103711 Tstd1 3.057 1.536 0.006804 0.00696
ENSMUSG00000025909 Sntg1 4.644 1.536 0.006815 0.006965
ENSMUSG00000099858 Gm6652 3.706 1.536 0.007078 0.007228
ENSMUSG00000104027 Gm37211 2.114 1.536 0.007224 0.007369
ENSMUSG00000042349 Ikbke 4.145 2.097 0.007244 0.007383
ENSMUSG00000100798 Gm19589 2.33 2.131 0.007903 0.008047
ENSMUSG00000026632 Tatdn3 2.197 2.107 0.008144 0.008285
ENSMUSG00000026208 Des 2.396 1.842 0.00821 0.008345
ENSMUSG00000099490 Gm18802 2.488 1.536 0.008321 0.00845
ENSMUSG00000096772 Gm22114 3.421 1.536 0.008461 0.008584
ENSMUSG00000057715 A830018L16Rik 4.971 1.931 0.008831 0.008952
ENSMUSG00000026558 Uck2 2.151 1.536 0.009278 0.009396
ENSMUSG00000103329 Gm42492 4.391 1.72 0.01088 0.01101
ENSMUSG00000100454 Gm28901 3.402 2.254 0.01121 0.01133
ENSMUSG00000103367 Gm38158 3.972 1.536 0.01186 0.01198
ENSMUSG00000100555 Gm8173 2.276 1.536 0.01214 0.01225
ENSMUSG00000103509 Gm38372 3.178 1.536 0.01354 0.01365
ENSMUSG00000089808 Gm7241 3.592 1.536 0.01645 0.01656
ENSMUSG00000044337 Ackr3 2.463 1.931 0.01773 0.01783
ENSMUSG00000026254 Eif4e2 5.314 3.038 0.01642 0.01654
ENSMUSG00000047369 Dnah14 4.281 1.536 0.01786 0.01794
ENSMUSG00000026604 Ptpn14 2.596 1.536 0.02086 0.02093
ENSMUSG00000026368 F13b 2.596 1.536 0.02637 0.02644
plotly::ggplotly(
countData %>% 
  dplyr::filter(., rownames(countData) %in% "ENSMUSG00000034066") %>%
  t() %>%
  as.data.frame() %>%
  tibble::rownames_to_column("samplename") %>%
  dplyr::select(., samplename, ENSMUSG00000034066 = V1) %>%
  left_join(., sampleGroups, by = "samplename") %>%
  ggplot() +
  geom_boxplot(aes(x = group, y = ENSMUSG00000034066,
                   fill = group)) +
  theme_bw()
)

Box plot showing mean differences in our gene of interest between tissue types.